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Variational Learning For Finite Inverted Dirichlet Mixture Models And Its Applications
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Book Synopsis Variational Learning for Finite Inverted Dirichlet Mixture Models and Its Applications by : Parisa Tirdad
Download or read book Variational Learning for Finite Inverted Dirichlet Mixture Models and Its Applications written by Parisa Tirdad and published by . This book was released on 2015 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustering is an important step in data mining, machine learning, computer vision and image processing. It is the process of assigning similar objects to the same subset. Among available clustering techniques, finite mixture models have been remarkably used, since they have the ability to consider prior knowledge about the data. Employing mixture models requires, choosing a standard distribution, determining the number of mixture components and estimating the model parameters. Currently, the combination of Gaussian distribution, as the standard distribution, and Expectation Maximization (EM), as the parameter estimator, has been widely used with mixture models. However, each of these choices has its own limitations. In this thesis, these limitations are discussed and addressed via defining a variational inference framework for finite inverted Dirichlet mixture model, which is able to provide a better capability in modeling multivariate positive data, that appear frequently in many real world applications. Finite inverted Dirichlet mixtures enable us to model high-dimensional, both symmetric and asymmetric data. Compared to the conventional expectation maximization (EM) algorithm, the variational approach has the following advantages: it is computationally more efficient, it converges fast, and is able to estimate the parameters and the number of the mixture model components, automatically and simultaneously. The experimental results validate the presented approach on different synthetic datasets and shows its performance for two interesting and challenging real world applications, namely natural scene categorization and human activity classification.
Book Synopsis Variational Learning for Finite Shifted-Scaled Dirichlet Mixture Model and Its Applications by : Zeinab Arjmandiasl
Download or read book Variational Learning for Finite Shifted-Scaled Dirichlet Mixture Model and Its Applications written by Zeinab Arjmandiasl and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the huge amount of data produced every day, the interest in data mining and machine learning techniques has been growing. Ongoing advancement of technology has made AI systems subject to different issues. Data clustering is an important aspect of data analysis which is the process of grouping similar observations in the same subset. Among known clustering techniques, finite mixture models have led to outstanding results that created an inspiration toward further exploration of various mixture models and applications. The main idea of this clustering technique is to fit a mixture of components generated from a predetermined probability distribution into the data through parameter approximation of the components. Therefore, choosing a proper distribution based on the type of the data is another crucial step in data analysis. Although the Gaussian distribution has been widely used with mixture models, the Dirichlet family of distributions have been known to achieve better results particularly when dealing with proportional and non-Gaussian data. Another crucial part in statistical modelling is the learning process. Among the conventional estimation approaches, Maximum Likelihood (ML) is widely used due to its simplicity in terms of implementation but it has some drawbacks, too. Bayesian approach has overcome some of the disadvantages of ML approach via taking prior knowledge into account. However, it creates new issues such as need for additional estimation methods due to the intractability of parameters' marginal probabilities. In this thesis, these limitations are discussed and addressed via defining a variational learning framework for finite shifted-scaled Dirichlet mixture model. The motivation behind applying variational inference is that compared to conventional Bayesian approach, it is much less computationally costly. Furthermore, in this method, the optimal number of components is estimated along with the parameter approximation automatically and simultaneously while convergence is guaranteed. The performance of our model, in terms of accuracy of clustering, is validated on real world challenging medical applications, including image processing, namely, Malaria detection, breast cancer diagnosis and cardiovascular disease detection as well as text-based spam email detection. Finally, in order to evaluate the merits of our model effectiveness, it is compared with four other widely used methods.
Book Synopsis A Study on Entropy-Based Variational Learning for Mixture Models by : Mohammad Sadegh Ahmadzadeh
Download or read book A Study on Entropy-Based Variational Learning for Mixture Models written by Mohammad Sadegh Ahmadzadeh and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, we observe a rapid growth of complex data in all formats due to the technological development. Thanks to the field of machine learning, we can automatically analyze and infer useful information from these data. In particular, data clustering is regarded as one of the most famous data analysis tools aiming at grouping data with similar patterns into the same cluster. Among existing clustering techniques, finite mixture models have shown great flexibility in data modeling. Mixture models are a common unsupervised learning technique that have been widely used to statistically approximate and analyse heterogenous data. The goal of using mixture models is to fit the data into an appropriate distribution. A crucial point is to estimate the prefect parameters of the distribution and the suitable number of clusters in the data. To do so, an entropy-based variational learning algorithm is proposed for the model selection (i.e. determination of the optimal number of components). We investigate if a given component is genuinely distributed according to a mixture model to select the optimal number of components that better suits our data. In our work, we have used the variational inference framework that overcomes the over-fitting problem of maximum likelihood approaches and at the same time convergence is guaranteed. In addition, it decreases the computational complexity of purely Bayesian approaches. In recent researches the main concern when deploying mixture models has been the choice of distributions. The effectiveness of Dirichlet family of distributions has been proved in recent studies especially for non-Gaussian data. In this thesis, an effective mixture model-based approach for clustering and modeling purposes has been proposed. Our contribution is the application of an entropy-based variational inference algorithm to learn the mixture models, namely, generalized inverted Dirichlet and inverted Beta-Liouville mixture models. The performance of the proposed model is evaluated on multiple real-world applications such as human activity recognition, images, texture and breast cancer datasets, where in each case we compare our results with popular and similar models.
Book Synopsis Variational Approaches For Learning Finite Scaled Dirichlet Mixture Models by : Hieu Nguyen Dinh
Download or read book Variational Approaches For Learning Finite Scaled Dirichlet Mixture Models written by Hieu Nguyen Dinh and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With a massive amount of data created on a daily basis, the ubiquitous demand for data analysis is undisputed. Recent development of technology has made machine learning techniques applicable to various problems. Particularly, we emphasize on cluster analysis, an important aspect of data analysis. Recent works with excellent results on the aforementioned task using finite mixture models have motivated us to further explore their extents with different applications. In other words, the main idea of mixture model is that the observations are generated from a mixture of components, in each of which the probability distribution should provide strong flexibility in order to fit numerous types of data. Indeed, the Dirichlet family of distributions has been known to achieve better clustering performances than those of Gaussian when the data are clearly non-Gaussian, especially proportional data. Thus, we introduce several variational approaches for finite Scaled Dirichlet mixture models. The proposed algorithms guarantee reaching convergence while avoiding the computational complexity of conventional Bayesian inference. In summary, our contributions are threefold. First, we propose a variational Bayesian learning framework for finite Scaled Dirichlet mixture models, in which the parameters and complexity of the models are naturally estimated through the process of minimizing the Kullback-Leibler (KL) divergence between the approximated posterior distribution and the true one. Secondly, we integrate component splitting into the first model, a local model selection scheme, which gradually splits the components based on their mixing weights to obtain the optimal number of components. Finally, an online variational inference framework for finite Scaled Dirichlet mixture models is developed by employing a stochastic approximation method in order to improve the scalability of finite mixture models for handling large scale data in real time. The effectiveness of our models is validated with real-life challenging problems including object, texture, and scene categorization, text-based and image-based spam email detection.
Book Synopsis Mixture Models and Applications by : Nizar Bouguila
Download or read book Mixture Models and Applications written by Nizar Bouguila and published by Springer. This book was released on 2019-08-13 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.
Book Synopsis A Study on Variational Component Splitting Approach for Mixture Models by : Kamal Maanicshah Mathin Henry
Download or read book A Study on Variational Component Splitting Approach for Mixture Models written by Kamal Maanicshah Mathin Henry and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Increase in use of mobile devices and the introduction of cloud-based services have resulted in the generation of enormous amount of data every day. This calls for the need to group these data appropriately into proper categories. Various clustering techniques have been introduced over the years to learn the patterns in data that might better facilitate the classification process. Finite mixture model is one of the crucial methods used for this task. The basic idea of mixture models is to fit the data at hand to an appropriate distribution. The design of mixture models hence involves finding the appropriate parameters of the distribution and estimating the number of clusters in the data. We use a variational component splitting framework to do this which could simultaneously learn the parameters of the model and estimate the number of components in the model. The variational algorithm helps to overcome the computational complexity of purely Bayesian approaches and the over fitting problems experienced with Maximum Likelihood approaches guaranteeing convergence. The choice of distribution remains the core concern of mixture models in recent research. The efficiency of Dirichlet family of distributions for this purpose has been proved in latest studies especially for non-Gaussian data. This led us to study the impact of variational component splitting approach on mixture models based on several distributions. Hence, our contribution is the application of variational component splitting approach to design finite mixture models based on inverted Dirichlet, generalized inverted Dirichlet and inverted Beta-Liouville distributions. In addition, we also incorporate a simultaneous feature selection approach for generalized inverted Dirichlet mixture model along with component splitting as another experimental contribution. We evaluate the performance of our models with various real-life applications such as object, scene, texture, speech and video categorization.
Book Synopsis Hidden Markov Models and Applications by : Nizar Bouguila
Download or read book Hidden Markov Models and Applications written by Nizar Bouguila and published by Springer Nature. This book was released on 2022-05-19 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.
Book Synopsis Intelligent Information and Database Systems by : Ngoc Thanh Nguyen
Download or read book Intelligent Information and Database Systems written by Ngoc Thanh Nguyen and published by Springer Nature. This book was released on 2021-04-04 with total page 867 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021, held in Phuket, Thailand, in April 2021.* The 67 full papers accepted for publication in these proceedings were carefully reviewed and selected from 291 submissions. The papers of the first volume are organized in the following topical sections: data mining methods and applications; machine learning methods; decision support and control systems; natural language processing; cybersecurity intelligent methods; computer vision techniques; computational imaging and vision; advanced data mining techniques and applications; intelligent and contextual systems; commonsense knowledge, reasoning and programming in artificial intelligence; data modelling and processing for industry 4.0; innovations in intelligent systems. *The conference was held virtually.
Book Synopsis Image Analysis and Recognition by : Fakhri Karray
Download or read book Image Analysis and Recognition written by Fakhri Karray and published by Springer. This book was released on 2019-09-26 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 11662 and 11663 constitutes the refereed proceedings of the 16th International Conference on Image Analysis and Recognition, ICIAR 2019, held in Waterloo, ON, Canada, in August 2019. The 58 full papers presented together with 24 short and 2 poster papers were carefully reviewed and selected from 142 submissions. The papers are organized in the following topical sections: Image Processing; Image Analysis; Signal Processing Techniques for Ultrasound Tissue Characterization and Imaging in Complex Biological Media; Advances in Deep Learning; Deep Learning on the Edge; Recognition; Applications; Medical Imaging and Analysis Using Deep Learning and Machine Intelligence; Image Analysis and Recognition for Automotive Industry; Adaptive Methods for Ultrasound Beamforming and Motion Estimation.
Book Synopsis Artificial Intelligence and Data Mining in Healthcare by : Malek Masmoudi
Download or read book Artificial Intelligence and Data Mining in Healthcare written by Malek Masmoudi and published by Springer Nature. This book was released on 2021-01-25 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.
Book Synopsis Artificial Intelligence Applications in Information and Communication Technologies by : Yacine Laalaoui
Download or read book Artificial Intelligence Applications in Information and Communication Technologies written by Yacine Laalaoui and published by Springer. This book was released on 2015-07-04 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation and Ontologies, and Multi-agent Systems. The main aim of this book is to help Information and Communication Technologies (ICT) practitioners in managing efficiently their platforms using AI tools and methods and to provide them with sufficient Artificial Intelligence background to deal with real-life problems.
Book Synopsis Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices by : Hamido Fujita
Download or read book Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices written by Hamido Fujita and published by Springer Nature. This book was released on 2021-07-19 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNAI 12798 and 12799 constitutes the thoroughly refereed proceedings of the 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, held virtually and in Kuala Lumpur, Malaysia, in July 2021. The 87 full papers and 19 short papers presented were carefully reviewed and selected from 145 submissions. The IEA/AIE 2021 conference will continue the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas. These areas include the following: Part I, Artificial Intelligence Practices: Knowledge discovery and pattern mining; artificial intelligence and machine learning; sematic, topology, and ontology models; medical and health-related applications; graphic and social network analysis; signal and bioinformatics processing; evolutionary computation; attack security; natural language and text processing; fuzzy inference and theory; and sensor and communication networks Part II, From Theory to Practice: Prediction and recommendation; data management, clustering and classification; robotics; knowledge based and decision support systems; multimedia applications; innovative applications of intelligent systems; CPS and industrial applications; defect, anomaly and intrusion detection; financial and supply chain applications; Bayesian networks; BigData and time series processing; and information retrieval and relation extraction
Book Synopsis Advances in Visual Computing by : George Bebis
Download or read book Advances in Visual Computing written by George Bebis and published by Springer Nature. This book was released on 2020-12-11 with total page 763 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 12509 and 12510 constitutes the refereed proceedings of the 15th International Symposium on Visual Computing, ISVC 2020, which was supposed to be held in San Diego, CA, USA in October 2020, took place virtually instead due to the COVID-19 pandemic. The 114 full and 4 short papers presented in these volumes were carefully reviewed and selected from 175 submissions. The papers are organized into the following topical sections: Part I: deep learning; segmentation; visualization; video analysis and event recognition; ST: computational bioimaging; applications; biometrics; motion and tracking; computer graphics; virtual reality; and ST: computer vision advances in geo-spatial applications and remote sensing Part II: object recognition/detection/categorization; 3D reconstruction; medical image analysis; vision for robotics; statistical pattern recognition; posters
Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Book Synopsis Artificial Intelligence and Data Mining in Healthcare by : Malek Masmoudi
Download or read book Artificial Intelligence and Data Mining in Healthcare written by Malek Masmoudi and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.
Book Synopsis Variational Mixture Models for Non-Gaussian Observations by : Stavroula Gerontogianni
Download or read book Variational Mixture Models for Non-Gaussian Observations written by Stavroula Gerontogianni and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Contributions to Finite Mixture Models with Applications by : Hok Shing Kwong
Download or read book Contributions to Finite Mixture Models with Applications written by Hok Shing Kwong and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
