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Matrix and Tensor Decompositions in Signal Processing

The second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as;

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Tensor Computation for Data Analysis

Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss;

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Non-Negative Matrix And Tensor Factorizations

in nonnegative matrix and tensor factorizations, as well as decompositions and sparse representation of data, will ensure that this book is essential;

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Tensors for Data Processing

data processing theories and methods. As a higher-order generalization of a matrix, tensor-based processing can avoid multi-linear;

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Matrix Calculus, Kronecker Product and Tensor Product - A Practical Approach to Linear Algebra, Multilinear Algebra and Tensor Calculus with Software Implementations (3rd Edition)

Kronecker product and tensor product. The Kronecker product has widespread applications in signal processing, discrete wavelets, statistical physics;

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Advanced Linear and Matrix Algebra

, multilinearity, and tensors. Concepts draw on concrete examples throughout, offering accessible pathways to advanced techniques. Beginning with a;

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Tensors in Image Processing and Computer Vision

Tensor signal processing is an emerging field with important applications to computer vision and image processing. This book presents;

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Latent Variable Analysis and Signal Separation

geometry estimation; tensors and audio; audio signal processing; theoretical developments; physics and bio signal processing; latent variable;

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Bilinear Regression Analysis

from linear models are extended and applied to tensor spaces. Further, the book considers decompositions of tensor products into natural;

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Understanding Complex Datasets

Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application;

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Blind Identification and Separation of Complex-valued Signals

University of Toulon, France. His research interests concern statistical signal processing, high order statistics and matrixtensor decompositions;

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Signal and Image Processing for Remote Sensing

representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental;

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Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace;

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Covariance Analysis for Seismic Signal Processing

matrix analysis in the processing of geophysical signals. A background of basic linear algebra, statistics, and fundamental random signal;

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Advances in Independent Component Analysis and Learning Machines

theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.

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Signal and Image Processing for Remote Sensing

sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new;

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From Algebraic Structures to Tensors

heterogeneous big data in numerous fields. This set on Matrices and Tensors in Signal Processing aims at giving a self-contained and comprehensive;

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New Approaches for Multidimensional Signal Processing

data mining, watermarking, hiding and encryption of MD images, MD image processing in robot systems, tensor-based data processing, 3D and multi;

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Signals and Images

Signals and Images: Advances and Results in Speech, Estimation, Compression, Recognition, Filtering, and Processing cohesively combines;

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Signals and Images

Signals and Images: Advances and Results in Speech, Estimation, Compression, Recognition, Filtering, and Processing cohesively combines;

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Digital Signal Processing 101

Processing, and haseight new chapters on: Automotive Radar Signal Processing Space-Time Adaptive Processing Radar

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Advanced Signal Analysis and its Applications to Mathematical Physics

The mathematical tools used in signal analysis involve differential and difference equations, integral equations, matrix algebra and;

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Signal Processing and Networking for Big Data Applications

This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents;

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Regularization, Optimization, Kernels, and Support Vector Machines

-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine;

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Regularization, Optimization, Kernels, and Support Vector Machines

portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents;

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Latent Variable Analysis and Signal Separation

This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015;

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Tensor Networks for Dimensionality Reduction and Large-scale Optimization

and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely;

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