Nonnegative Matrix and Tensor Factorizations - Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMFs various extensions and modifications, especially Nonnegative Tensor…
Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications
This volume unifies and extends the theories of adaptive blind signal and image processing and provides practical and efficient algorithms for blind source separation, Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and Equalization.
Adaptive blind signal and image processing
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A New Learning Algorithm for Blind Signal Separation
A new on-line learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals and has an equivariant property and is easily implemented on a neural network like model.
Neural networks for optimization and signal processing
A guide to the fundamental mathematics of neurocomputing, a review of neural network models and an analysis of their associated algorithms, and state-of-the-art procedures to solve optimization problems are explained.
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them, and identifies a number of challenges to further advance EEG classification in BCI.
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
- Qibin Zhao, Liqing Zhang, A. Cichocki
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 25 January 2014
The method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries, which outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.
Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives
Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations
A class of optimized local algorithms which are referred to as Hierarchical Alternating Least Squares (HALS) algorithms, which work well for NMF-based blind source separation (BSS) not only for the over-determined case but also for an under-d determined (over-complete) case if data are sufficiently sparse.
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis
Benefiting from the power of multilinear algebra as their mathematical backbone, data analysis techniques using tensor decompositions are shown to have great flexibility in the choice of constraints which match data properties and extract more general latent components in the data than matrix-based methods.