Latent Factor Analysis For High Dimensional And Sparse Matrices

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Latent Factor Analysis for High-dimensional and Sparse Matrices

Author : Ye Yuan
Publisher : Springer Nature
ISBN 13 : 9811967032
Total Pages : 99 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Latent Factor Analysis for High-dimensional and Sparse Matrices by : Ye Yuan

Download or read book Latent Factor Analysis for High-dimensional and Sparse Matrices written by Ye Yuan and published by Springer Nature. This book was released on 2022-11-15 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.

Mobile Computing and Sustainable Informatics

Author : Subarna Shakya
Publisher : Springer Nature
ISBN 13 : 9819908353
Total Pages : 792 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Mobile Computing and Sustainable Informatics by : Subarna Shakya

Download or read book Mobile Computing and Sustainable Informatics written by Subarna Shakya and published by Springer Nature. This book was released on 2023-05-26 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected high-quality research papers presented at International Conference on Mobile Computing and Sustainable Informatics (ICMCSI 2022) organized by Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal, during January 11–12, 2023. The book discusses recent developments in mobile communication technologies ranging from mobile edge computing devices to personalized, embedded, and sustainable applications. The book covers vital topics like mobile networks, computing models, algorithms, sustainable models, and advanced informatics that support the symbiosis of mobile computing and sustainable informatics.

PRICAI 2023: Trends in Artificial Intelligence

Author : Fenrong Liu
Publisher : Springer Nature
ISBN 13 : 9819970199
Total Pages : 525 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis PRICAI 2023: Trends in Artificial Intelligence by : Fenrong Liu

Download or read book PRICAI 2023: Trends in Artificial Intelligence written by Fenrong Liu and published by Springer Nature. This book was released on 2023-11-10 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set, LNCS 14325-14327 constitutes the thoroughly refereed proceedings of the 20th Pacific Rim Conference on Artificial Intelligence, PRICAI 2023, held in Jakarta, Indonesia, in November 2023. The 95 full papers and 36 short papers presented in these volumes were carefully reviewed and selected from 422 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc.

Robust Latent Feature Learning for Incomplete Big Data

Author : Di Wu
Publisher : Springer Nature
ISBN 13 : 981198140X
Total Pages : 119 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Robust Latent Feature Learning for Incomplete Big Data by : Di Wu

Download or read book Robust Latent Feature Learning for Incomplete Big Data written by Di Wu and published by Springer Nature. This book was released on 2022-12-06 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.

Robot Control and Calibration

Author : Xin Luo
Publisher : Springer Nature
ISBN 13 : 9819957664
Total Pages : 132 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Robot Control and Calibration by : Xin Luo

Download or read book Robot Control and Calibration written by Xin Luo and published by Springer Nature. This book was released on 2023-09-25 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; RNN for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration – Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm – which can effectively enhance robot positioning accuracy. In addition, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies without calibration data. Thus, this book provides a publicly available dataset to assist researchers from other fields in conducting calibration experiments and validating their ideas. The book also discusses six regularization schemes based on its robot error models, i.e., L1, L2, dropout, elastic, log, and swish. Robots’ positioning accuracy is significantly improved after calibration. Using the control and calibration methods developed here, readers will be ready to conduct their own research and experiments.

Dynamic Network Representation Based on Latent Factorization of Tensors

Author : Hao Wu
Publisher : Springer Nature
ISBN 13 : 9811989346
Total Pages : 89 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Dynamic Network Representation Based on Latent Factorization of Tensors by : Hao Wu

Download or read book Dynamic Network Representation Based on Latent Factorization of Tensors written by Hao Wu and published by Springer Nature. This book was released on 2023-03-07 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes’ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge. In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.

Machine Learning and Knowledge Discovery in Databases: Research Track

Author : Danai Koutra
Publisher : Springer Nature
ISBN 13 : 3031434242
Total Pages : 506 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases: Research Track by : Danai Koutra

Download or read book Machine Learning and Knowledge Discovery in Databases: Research Track written by Danai Koutra and published by Springer Nature. This book was released on 2023-09-17 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: Robustness; Time Series; Transfer and Multitask Learning. Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Behavior Design And Analysis

Author : Yinyan Zhang
Publisher : Springer Nature
ISBN 13 : 9811532311
Total Pages : 193 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Machine Behavior Design And Analysis by : Yinyan Zhang

Download or read book Machine Behavior Design And Analysis written by Yinyan Zhang and published by Springer Nature. This book was released on 2020-03-13 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we present our systematic investigations into consensus in multi-agent systems. We show the design and analysis of various types of consensus protocols from a multi-agent perspective with a focus on min-consensus and its variants. We also discuss second-order and high-order min-consensus. A very interesting topic regarding the link between consensus and path planning is also included. We show that a biased min-consensus protocol can lead to the path planning phenomenon, which means that the complexity of shortest path planning can emerge from a perturbed version of min-consensus protocol, which as a case study may encourage researchers in the field of distributed control to rethink the nature of complexity and the distance between control and intelligence. We also illustrate the design and analysis of consensus protocols for nonlinear multi-agent systems derived from an optimal control formulation, which do not require solving a Hamilton-Jacobi-Bellman (HJB) equation. The book was written in a self-contained format. For each consensus protocol, the performance is verified through simulative examples and analyzed via mathematical derivations, using tools like graph theory and modern control theory. The book’s goal is to provide not only theoretical contributions but also explore underlying intuitions from a methodological perspective.

Statistical Analysis for High-Dimensional Data

Author : Arnoldo Frigessi
Publisher : Springer
ISBN 13 : 3319270990
Total Pages : 313 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Statistical Analysis for High-Dimensional Data by : Arnoldo Frigessi

Download or read book Statistical Analysis for High-Dimensional Data written by Arnoldo Frigessi and published by Springer. This book was released on 2016-02-16 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.

Learning Automata and Their Applications to Intelligent Systems

Author : JunQi Zhang
Publisher : John Wiley & Sons
ISBN 13 : 1394188528
Total Pages : 276 pages
Book Rating : 4.3/5 (941 download)

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Book Synopsis Learning Automata and Their Applications to Intelligent Systems by : JunQi Zhang

Download or read book Learning Automata and Their Applications to Intelligent Systems written by JunQi Zhang and published by John Wiley & Sons. This book was released on 2023-11-10 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive guide on learning automata, introducing two variants to accelerate convergence and computational update speed Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively; these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata. As noisy optimization problems exist widely in various intelligent systems, this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application. The existing and most representative applications of learning automata include classification, clustering, game, knapsack, network, optimization, ranking, and scheduling. They are well-discussed. Future research directions to promote an intelligent system are suggested. Written by two highly qualified academics with significant experience in the field, Learning Automata and Their Applications to Intelligent Systems covers such topics as: Mathematical analysis of the behavior of learning automata, along with suitable learning algorithms Two application-oriented learning automata: one to discover and track spatiotemporal event patterns, and the other to solve stochastic searching on a line Demonstrations of two pioneering variants of Optimal Computing Budge Allocation (OCBA) methods and how to combine learning automata with ordinal optimization How to achieve significantly faster convergence and higher accuracy than classical pursuit schemes via lower computational complexity of updating the state probability A timely text in a rapidly developing field, Learning Automata and Their Applications to Intelligent Systems is an essential resource for researchers in machine learning, engineering, operation, and management. The book is also highly suitable for graduate level courses on machine learning, soft computing, reinforcement learning and stochastic optimization.

Advances in Computational Intelligence Systems

Author : Thomas Jansen
Publisher : Springer Nature
ISBN 13 : 3030870944
Total Pages : 579 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Advances in Computational Intelligence Systems by : Thomas Jansen

Download or read book Advances in Computational Intelligence Systems written by Thomas Jansen and published by Springer Nature. This book was released on 2021-11-17 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the papers presented at the 20th UK Workshop on Computational Intelligence (UKCI 2021), held virtually by Aberystwyth University, 8–10th September 2021. This marks the 20th anniversary of UKCI; a testament to the increasing role and importance of Computational Intelligence (CI) and the continuing interest in its development. UKCI provides a forum for the academic community and industry to share ideas and experience in this field. EDMA 2021, the 4th International Engineering Data- and Model-Driven Applications workshop, is also incorporated and held in conjunction with UKCI 2021. Paper submissions were invited in the areas of fuzzy systems, neural networks, evolutionary computation, machine learning, data mining, cognitive computing, intelligent robotics, hybrid methods, deep learning and applications of CI.

Towards Neuroscience-Inspired Intelligent Computing: Theory, Methods, and Applications

Author : Di Wu
Publisher : Frontiers Media SA
ISBN 13 : 2832519172
Total Pages : 136 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Towards Neuroscience-Inspired Intelligent Computing: Theory, Methods, and Applications by : Di Wu

Download or read book Towards Neuroscience-Inspired Intelligent Computing: Theory, Methods, and Applications written by Di Wu and published by Frontiers Media SA. This book was released on 2023-04-03 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Services – SERVICES 2021

Author : Mohamed Adel Serhani
Publisher : Springer Nature
ISBN 13 : 3030965856
Total Pages : 125 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Services – SERVICES 2021 by : Mohamed Adel Serhani

Download or read book Services – SERVICES 2021 written by Mohamed Adel Serhani and published by Springer Nature. This book was released on 2022-02-12 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th World Congress on Services, SERVICES 2021, held as part of the Services Conference Federation, SCF 2021, in December 2021. The conference was held virtually due to the COVID-19 pandemic. The 7 full papers and 2 short papers presented were carefully reviewed and selected from 43 submissions. The papers cover topics in the field of Advertising Services, Banking Services, Broadcasting & Cable TV Service, Business Services, Communications Services, Government Services, Real Estate Operations Services, Schools and Education Services, Healthcare Services, and much more.

Data Mining for Bioinformatics

Author : Sumeet Dua
Publisher : CRC Press
ISBN 13 : 0849328012
Total Pages : 351 pages
Book Rating : 4.8/5 (493 download)

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Book Synopsis Data Mining for Bioinformatics by : Sumeet Dua

Download or read book Data Mining for Bioinformatics written by Sumeet Dua and published by CRC Press. This book was released on 2012-11-06 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field. The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections: Supplies a complete overview of the evolution of the field and its intersection with computational learning Describes the role of data mining in analyzing large biological databases—explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data Covers supervised learning using classification techniques most commonly used in bioinformatics—addressing the need for validation and benchmarking of inferences derived using either clustering or classification The book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biological databases, it considers systems of both single and ensemble classifiers and shares effort-saving tips for model selection and performance estimation strategies.

Bayesian Statistics 7

Author : J. M. Bernardo
Publisher : Oxford University Press
ISBN 13 : 9780198526155
Total Pages : 1114 pages
Book Rating : 4.5/5 (261 download)

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Book Synopsis Bayesian Statistics 7 by : J. M. Bernardo

Download or read book Bayesian Statistics 7 written by J. M. Bernardo and published by Oxford University Press. This book was released on 2003-07-03 with total page 1114 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the 7th Valencia International Meeting on Bayesian Statistics. This conference is held every four years and provides the main forum for researchers in the area of Bayesian statistics to come together to present and discuss frontier developments in the field.

Collaborative Computing: Networking, Applications and Worksharing

Author : Honghao Gao
Publisher : Springer Nature
ISBN 13 : 3030926354
Total Pages : 757 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Collaborative Computing: Networking, Applications and Worksharing by : Honghao Gao

Download or read book Collaborative Computing: Networking, Applications and Worksharing written by Honghao Gao and published by Springer Nature. This book was released on 2022-01-01 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the refereed proceedings of the 17th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 62 full papers and 7 short papers presented were carefully reviewed and selected from 206 submissions. The papers reflect the conference sessions as follows: Optimization for Collaborate System; Optimization based on Collaborative Computing; UVA and Traffic system; Recommendation System; Recommendation System & Network and Security; Network and Security; Network and Security & IoT and Social Networks; IoT and Social Networks & Images handling and human recognition; Images handling and human recognition & Edge Computing; Edge Computing; Edge Computing & Collaborative working; Collaborative working & Deep Learning and application; Deep Learning and application; Deep Learning and application; Deep Learning and application & UVA.

Probabilistic Approaches to Recommendations

Author : Nicola Barbieri
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627052585
Total Pages : 199 pages
Book Rating : 4.6/5 (27 download)

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Book Synopsis Probabilistic Approaches to Recommendations by : Nicola Barbieri

Download or read book Probabilistic Approaches to Recommendations written by Nicola Barbieri and published by Morgan & Claypool Publishers. This book was released on 2014-05-01 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available. Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy. We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations.