By Andrzej Cichocki, Rafal Zdunek, Anh Huy Phan, Shun-ichi Amari
This ebook offers a huge survey of versions and effective algorithms for Nonnegative Matrix Factorization (NMF). This comprises NMF’s numerous extensions and variations, specially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF/NTF and their extensions are more and more used as instruments in sign and snapshot processing, and knowledge research, having garnered curiosity as a result of their potential to supply new insights and appropriate information regarding the advanced latent relationships in experimental info units. it is recommended that NMF offers significant elements with actual interpretations; for instance, in bioinformatics, NMF and its extensions were effectively utilized to gene expression, series research, the useful characterization of genes, clustering and textual content mining. As such, the authors specialise in the algorithms which are most valuable in perform, the quickest, so much powerful, and compatible for large-scale types.
Key positive aspects:
- Acts as a unmarried resource reference advisor to NMF, collating details that's largely dispersed in present literature, together with the authors’ personal lately built suggestions within the topic zone.
- Uses generalized expense features akin to Bregman, Alpha and Beta divergences, to offer functional implementations of various kinds of powerful algorithms, specifically Multiplicative, Alternating Least Squares, Projected Gradient and Quasi Newton algorithms.
- Provides a comparative research of the various tools as a way to determine approximation mistakes and complexity.
- Includes pseudo codes and optimized MATLAB resource codes for the majority algorithms offered within the booklet.
The expanding curiosity in nonnegative matrix and tensor factorizations, in addition to decompositions and sparse illustration of information, will make sure that this publication is vital examining for engineers, scientists, researchers, practitioners and graduate scholars throughout sign and picture processing; neuroscience; facts mining and information research; machine technology; bioinformatics; speech processing; biomedical engineering; and multimedia.