Download or read book Statistical Signal Processing of Nonstationary Tensor valued Data written by Bruno Scalzo Dees and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Tensor Computation for Data Analysis written by Yipeng Liu and published by Springer Nature. This book was released on 2021-08-31 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss in classical matrix computation-based data analysis. This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis, and benefits researchers, engineers, and students with theoretical, computational, technical and experimental details. It presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, and comprehensive coverage of tensor computation based data analysis techniques. In addition, some practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering are also presented for easy understanding and implementation. These data analysis techniques may be further applied in other applications on neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc. The discussion begins with basic coverage of notations, preliminary operations in tensor computations, main tensor decompositions and their properties. Based on them, a series of tensor-based data analysis techniques are presented as the tensor extensions of their classical matrix counterparts, including tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, robust principal tensor component analysis, tensor regression, logistical tensor regression, support tensor machine, multilinear discriminate analysis, tensor subspace clustering, tensor-based deep learning, tensor graphical model and tensor sketch. The discussion also includes a number of typical applications with experimental results, such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, link prediction, environmental prediction, weather forecasting, background extraction, human pose estimation, cognitive state classification from fMRI, infrared small target detection, heterogeneous information networks clustering, multi-view image clustering, and deep neural network compression.
Download or read book Statistical Signal Processing written by Edward J. Wegman and published by . This book was released on 1984 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Matrix and Tensor Decompositions in Signal Processing Volume 2 written by Gérard Favier and published by John Wiley & Sons. This book was released on 2021-08-31 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the main tensor decompositions. After a brief historical review of the compressed sampling methods, an overview of the main methods of retrieving matrices and tensors with missing data will be performed under the low rank hypothesis. Illustrative examples will be provided.
Download or read book Handbook of Blind Source Separation written by Pierre Comon and published by Academic Press. This book was released on 2010-02-17 with total page 856 pages. Available in PDF, EPUB and Kindle. Book excerpt: Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications
Download or read book Improving and Unfolding Statistical Models of Nonstationary Signals written by Scott Thomas Wisdom and published by . This book was released on 2017 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improving the modeling and processing of nonstationary signals remains an important yet challenging problem. In the past, the most effective approach for processing these signals has been statistical modeling. Statistical models can effectively encode domain knowledge and lead to principled algorithms for the fundamental tasks of enhancement, detection, and classification. However, the performance of statistical models can be limited because they inherently make assumptions about the distribution of the data. Deep neural networks, in contrast, have recently outperformed state-of-the-art statistical models of nonstationary signals. Deep neural networks are completely data-driven, and learn to set their parameters by training on large datasets that are assumed to match the distribution of the data. This dissertation follows two approaches for improving modeling and processing of nonstationary signals. The first approach examines conventional model assumptions and suggests improvements that lead to improved performance for processing nonstationary signals. Specifically, noncircular distributions of the complex-valued short-time Fourier transform are shown to improve detection of realistic nonstationary signals. Then the parameterization of a recently-proposed recurrent neural network for processing nonstationary signals is reexamined. By using an optimization method that preserves the capacity of the recurrence matrix, superior performance is achieved on a battery of benchmarks that test the ability of recurrent neural networks to process nonstationary signals. The second approach uses the recently-proposed framework of deep unfolding, which provides a principled means of transforming statistical model inference algorithms into deep networks. This dissertation expands the deep unfolding framework specifically for nonstationary signals. Using this framework, a model-based explanation is provided for state-of-the-art recurrent neural architectures, including gated recurrent unit and unitary recurrent neural networks. Additionally, deep unfolding results in deep network architectures that arise in principled ways from statistical model assumptions. This statistical model foundation provides initializations for the unfolded networks, which lead to better generalization, faster training, and competitive or superior performance on a variety of tasks, including single- and multichannel acoustic source separation and classification of acoustic signals.
Download or read book Tensors for Data Processing written by Yipeng Liu and published by Elsevier. This book was released on 2021-10-27 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tensors for Data Processing: Theory, Methods and Applications presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation theories, processing methods, computing and engineering applications, with an emphasis on techniques for data processing. This reference is ideal for students, researchers and industry developers who want to understand and use tensor-based data processing theories and methods. As a higher-order generalization of a matrix, tensor-based processing can avoid multi-linear data structure loss that occurs in classical matrix-based data processing methods. This move from matrix to tensors is beneficial for many diverse application areas, including signal processing, computer science, acoustics, neuroscience, communication, medical engineering, seismology, psychometric, chemometrics, biometric, quantum physics and quantum chemistry. Provides a complete reference on classical and state-of-the-art tensor-based methods for data processing Includes a wide range of applications from different disciplines Gives guidance for their application
Download or read book Advances in Statistical Signal Processing written by H. Vincent Poor and published by JAI Press(NY). This book was released on 1987 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Non uniform Sampling in Statistical Signal Processing written by Frida Eng and published by . This book was released on 2007 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Studies in Statistical Signal Processing written by Thomas Kailath and published by . This book was released on 1986 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary objective of our research is to develop efficient and numerically stable algorithms for nonstationary signal processing problems by understanding and exploiting special structures, both deterministic and stochastic, in the problems. We also strive to establish and broaden links with related disciplines, such as cascade filter synthesis, scattering theory, numerical linear algebra, and mathematical operator theory for the purpose of cross fertilization of ideas and techniques. These explorations have led to new results both in estimation theory and in these other fields, e.g., to new orthogonal cascade digital filter structures, new algorithms for triangular and QR factorization of structured matrices and new techniques for stability testing. For several years, the guiding principle in these studies has been the concept of (Toeplitz-oriented) displacement structure (Kailath, Kung and Morf, (1979)), which generalized and subsumed our earlier work on fast (Chandrasekhar) control and estimation algorithms for state-space models (Morf, Sidhu and Kailath, (1974)). Several authors have since picked up these ideas in a number of fields. A notable such work is a recent book by Heinig and K. Rost of East Germany, entitled 'Algebraic Methods for Toeplitz-Like Matrices and Operators'.
Download or read book Academic Press Library in Signal Processing written by Paulo S.R. Diniz and published by Academic Press. This book was released on 2013-09-21 with total page 1559 pages. Available in PDF, EPUB and Kindle. Book excerpt: This first volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in machine learning and advanced signal processing theory. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved Quick tutorial reviews of important and emerging topics of research in machine learning Presents core principles in signal processing theory and shows their applications Reference content on core principles, technologies, algorithms and applications Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic
Download or read book Academic Press Library in Signal Processing written by Mats Viberg and published by Academic Press. This book was released on 2013-08-31 with total page 1013 pages. Available in PDF, EPUB and Kindle. Book excerpt: This third volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in array and statistical signal processing. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved Quick tutorial reviews of important and emerging topics of research in array and statistical signal processing Presents core principles and shows their application Reference content on core principles, technologies, algorithms and applications Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic
Download or read book Mathematical Methods for Signal and Image Analysis and Representation written by Luc Florack and published by Springer Science & Business Media. This book was released on 2012-01-13 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Methods for Signal and Image Analysis and Representation presents the mathematical methodology for generic image analysis tasks. In the context of this book an image may be any m-dimensional empirical signal living on an n-dimensional smooth manifold (typically, but not necessarily, a subset of spacetime). The existing literature on image methodology is rather scattered and often limited to either a deterministic or a statistical point of view. In contrast, this book brings together these seemingly different points of view in order to stress their conceptual relations and formal analogies. Furthermore, it does not focus on specific applications, although some are detailed for the sake of illustration, but on the methodological frameworks on which such applications are built, making it an ideal companion for those seeking a rigorous methodological basis for specific algorithms as well as for those interested in the fundamental methodology per se. Covering many topics at the forefront of current research, including anisotropic diffusion filtering of tensor fields, this book will be of particular interest to graduate and postgraduate students and researchers in the fields of computer vision, medical imaging and visual perception.
Download or read book Signal Processing Techniques for Computational Health Informatics written by Md Atiqur Rahman Ahad and published by Springer Nature. This book was released on 2020-10-07 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on signal processing techniques used in computational health informatics. As computational health informatics is the interdisciplinary study of the design, development, adoption and application of information and technology-based innovations, specifically, computational techniques that are relevant in health care, the book covers a comprehensive and representative range of signal processing techniques used in biomedical applications, including: bio-signal origin and dynamics, sensors used for data acquisition, artefact and noise removal techniques, feature extraction techniques in the time, frequency, time–frequency and complexity domain, and image processing techniques in different image modalities. Moreover, it includes an extensive discussion of security and privacy challenges, opportunities and future directions for computational health informatics in the big data age, and addresses the incorporation of recent techniques from the areas of artificial intelligence, deep learning and human–computer interaction. The systematic analysis of the state-of-the-art techniques covered here helps to further our understanding of the physiological processes involved and expandour capabilities in medical diagnosis and prognosis. In closing, the book, the first of its kind, blends state-of-the-art theory and practices of signal processing techniques inthe health informatics domain with real-world case studies building on those theories. As a result, it can be used as a text for health informatics courses to provide medics with cutting-edge signal processing techniques, or to introducehealth professionals who are already serving in this sector to some of the most exciting computational ideas that paved the way for the development of computational health informatics.
Download or read book Unsupervised Learning Methods for Statistical Signal Processing written by Roland Vollgraf and published by . This book was released on 2006 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Signal Processing and Control written by Björn Ottersten and published by . This book was released on 1993 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Some Contributions to Statistical Signal Processing and Machine Learning written by Qi Gao and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid development of science and information technology, the amount of data has been growing at an unprecedented rate, which brings many interesting yet challenging problems for modern statistical research. We are faced with increasing dimensionality and complexity of data, which often requires new techniques to approach classical problems such as regression and model selection. In this dissertation, we discuss and propose solutions to three problems in the realm of statistical signal processing and machine learning with applications in high dimensional data. The first problem considers nonparametric modeling and break point detection for time series signal of counts using a genetic algorithm paired with radial basis expansion. We then study high dimensional variable selection in regression and classification with missing data while the missing fraction can be relatively large. Lastly, generalized fiducial inference for high-dimensional sparse additive models based on a spline representation is presented.
