Download or read book Exploring Low rank Prior in High dimensional Data written by He Lyu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-dimensional data plays a ubiquitous role in real applications, ranging from biology, computer vision, to social media. The large dimensionality poses new challenges on statistical methods due to the "curse of dimensionality". To overcome these challenges, many statistical and machine learning approaches have been developed based on imposing additional assumptions on the data. One popular assumption is the low-rank prior, which assumes the high-dimensional data lies in a low-dimensional subspace, and approximately exhibits low-rank structure.In this dissertation, we explore various applications of low-rank prior. Chapter 2 studies the stability of leading singular subspaces. Various widely used algorithms have been proposed in numerical analysis, matrix completion, and matrix denoising based on the low-rank assumption, such as Principal Component Analysis and Singular Value Hard Thresholding. Many of these methods involve the computation of Singular Value Decomposition (SVD). To study the stability of these algorithms, in Chapter 2 we establish a useful set of formulae for the sinÎ8 distance between the original and the perturbed singular subspaces. Following this, we further derive a collection of new results on SVD perturbation related problems.In Chapter 3, we employ the low-rank prior for manifold denoising problems. Specifically, we generalize the Robust PCA (RPCA) method to manifold setting and propose an optimization framework that separates the sparse component from the noisy data. It is worth noting that in this chapter, we generalize the low-rank prior to a more general form to accommodate data with a more complex structure, instead of assuming the data itself lies in a low-dimensional subspace as in RPCA, we assume the clean data is distributed around a low-dimensional manifold. Therefore, if we consider a local neighborhood, the sub-matrix will be approximately low rank.Subsequently, in Chapter 4 we study the stability of invariant subspaces for eigensystems. Specifically, we focus on the case where the eigensystem is ill-conditioned and explore how the condition numbers affect the stability of invariant subspaces.The material presented in this dissertation encompasses several publications and preprints in the fields of Statistical, Numerical Linear Algebra, and Machine Learning, including Lyu and Wang (2020a); Lyu et al. (2019); Lyu and Wang (2022).

Download or read book Exploration and Analysis of DNA Microarray and Other High Dimensional Data written by Dhammika Amaratunga and published by John Wiley & Sons. This book was released on 2014-01-27 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition “...extremely well written...a comprehensive and up-to-date overview of this important field.” – Journal of Environmental Quality Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition provides comprehensive coverage of recent advancements in microarray data analysis. A cutting-edge guide, the Second Edition demonstrates various methodologies for analyzing data in biomedical research and offers an overview of the modern techniques used in microarray technology to study patterns of gene activity. The new edition answers the need for an efficient outline of all phases of this revolutionary analytical technique, from preprocessing to the analysis stage. Utilizing research and experience from highly-qualified authors in fields of data analysis, Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition features: A new chapter on the interpretation of findings that includes a discussion of signatures and material on gene set analysis, including network analysis New topics of coverage including ABC clustering, biclustering, partial least squares, penalized methods, ensemble methods, and enriched ensemble methods Updated exercises to deepen knowledge of the presented material and provide readers with resources for further study The book is an ideal reference for scientists in biomedical and genomics research fields who analyze DNA microarrays and protein array data, as well as statisticians and bioinformatics practitioners. Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition is also a useful text for graduate-level courses on statistics, computational biology, and bioinformatics.

Download or read book Advances and applications of artificial intelligence in geoscience and remote sensing written by Peng Zhenming and published by Frontiers Media SA. This book was released on 2023-08-30 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Low Rank and Sparse Modeling for Visual Analysis written by Yun Fu and published by Springer. This book was released on 2014-10-30 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

Download or read book Proceedings of the 3rd International Conference on Internet Finance and Digital Economy ICIFDE 2023 written by Yusheng Jiao and published by Springer Nature. This book was released on 2023-11-26 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book.With the advent of economic globalization and the information technology revolution, especially the dawn of the era of network economy marked by the Internet, human society is embarking on a transition from an industrial society to an information society, and from industrial civilization to information civilization. In recent years, domestic Internet business has become more and more prosperous. In order to adapt to the development of the new media era, many traditional industries have extended their business to the Internet field. Among them, the most prominent is the financial business. Thanks to the operation of Internet business, the efficiency of financial services has been rapidly improved. However, the rapid development of the Internet has also brought certain practical problems that must be faced. This conference has therefore been convened in the hope of engaging in an in-depth exchange with scholars in the following aspects: Acquaint yourself with the development status of Internet finance and the digital economy in various countries and deepen the elaboration of the concept of financial Internet; Summarize the characteristics of Internet finance in the world and propose solutions to the problems faced by Internet finance; Understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote the industrialization cooperation of academic achievements; Promote the institutionalization and standardization of management science through modern research. The previous conference of ICIFDE took place in Guangzhou, China (Online). ICIFDE 2023 will come back this year on August 04–06 and it will provide a valuable and face-to-face opportunity for researchers, scholars and some scientists to exchange their ideas. Distinguished by its strong organizational team, dependable reputation and prestigious sponsors across the globe, ICIFDE 2023 is an annual conference on Internet Finance and the Digital Economy for all researchers, both domestic and international. ICIFDE started in 2021, and all papers accepted in the last session of ICIFDE have been successfully published. The 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023) will be held in Chengdu, China on August 04–06, 2023. We warmly invite you to participate in ICIFDE 2023 and look forward to seeing you in Chengdu, China.

Download or read book Algorithms and Architectures for Parallel Processing written by Sheng Wen and published by Springer Nature. This book was released on 2020-01-21 with total page 711 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 11944-11945 constitutes the proceedings of the 19th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2019, held in Melbourne, Australia, in December 2019. The 73 full and 29 short papers presented were carefully reviewed and selected from 251 submissions. The papers are organized in topical sections on: Parallel and Distributed Architectures, Software Systems and Programming Models, Distributed and Parallel and Network-based Computing, Big Data and its Applications, Distributed and Parallel Algorithms, Applications of Distributed and Parallel Computing, Service Dependability and Security, IoT and CPS Computing, Performance Modelling and Evaluation.

Download or read book Computational Science and Its Applications ICCSA 2019 written by Sanjay Misra and published by Springer. This book was released on 2019-06-28 with total page 845 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volumes LNCS 11619-11624 constitute the refereed proceedings of the 19th International Conference on Computational Science and Its Applications, ICCSA 2019, held in Saint Petersburg, Russia, in July 2019. The 64 full papers, 10 short papers and 259 workshop papers presented were carefully reviewed and selected form numerous submissions. The 64 full papers are organized in the following five general tracks: computational methods, algorithms and scientific applications; high performance computing and networks; geometric modeling, graphics and visualization; advanced and emerging applications; and information systems and technologies. The 259 workshop papers were presented at 33 workshops in various areas of computational sciences, ranging from computational science technologies to specific areas of computational sciences, such as software engineering, security, artificial intelligence and blockchain technologies.

Download or read book The Recent Advances in Transdisciplinary Data Science written by Henry Han and published by Springer Nature. This book was released on 2023-01-28 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First Southwest Data Science Conference, on The Recent Advances in Transdisciplinary Data Science, SDSC 2022, held in Waco, TX, USA, during March 25–26, 2022. The 14 full papers and 2 short papers included in this book were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Business and social data science; Health and biological data science; Applied data science, artificial intelligence, and data engineering.

Download or read book High Dimensional Covariance Estimation written by Mohsen Pourahmadi and published by John Wiley & Sons. This book was released on 2013-05-28 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.

Download or read book Computational Intelligence Methods for Bioinformatics and Biostatistics written by Paolo Cazzaniga and published by Springer Nature. This book was released on 2020-12-09 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019, which was held in Bergamo, Italy, during September 4-6, 2019. The 28 full papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are grouped in topical sections as follows: Computational Intelligence Methods for Bioinformatics and Biostatistics; Algebraic and Computational Methods for the Study of RNA Behaviour; Intelligence methods for molecular characterization medicine; Machine Learning in Healthcare Informatics and Medical Biology; Modeling and Simulation Methods for Computational Biology and Systems Medicine.

Download or read book Image and Graphics Technologies and Applications written by Yongtian Wang and published by Springer Nature. This book was released on 2020-12-22 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 15th Conference on Image and Graphics Technologies and Applications, IGTA 2020, held in Beijing, China in September, 2020.* The 24 papers presented were carefully reviewed and selected from 115 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR, as well as image technology applications. *The conference was held virtually due to the COVID-19 pandemic.

Download or read book Generalized Low Rank Models written by Madeleine Udell and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. This dissertation extends the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features. We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results.

Download or read book Advances in Diagnostics and Treatment of Functional Neurological Disorders Neurogenomics Neuromodulation and Machine Learning written by Guohui Lu and published by Frontiers Media SA. This book was released on 2022-11-03 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Inference from High Dimensional Data written by Carlos Fernandez-Lozano and published by MDPI. This book was released on 2021-04-28 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: • Real-world problems can be high-dimensional, complex, and noisy • More data does not imply more information • Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information • A process with multidimensional information is not necessarily easy to interpret nor process • In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually the truth • The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data • The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens up a new era for integrative data-driven approaches • Entropy-based approaches are of interest to reduce the dimensionality of high-dimensional data

Download or read book Handbook of Robust Low Rank and Sparse Matrix Decomposition written by Thierry Bouwmans and published by CRC Press. This book was released on 2016-09-20 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

Download or read book Deep Learning through Sparse and Low Rank Modeling written by Zhangyang Wang and published by Academic Press. This book was released on 2019-04-11 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications

Download or read book Machine Learning and Knowledge Discovery in Databases written by Massih-Reza Amini and published by Springer Nature. This book was released on 2023-03-16 with total page 669 pages. Available in PDF, EPUB and Kindle. Book excerpt: The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.