@article{922439,
      recid = {922439},
      author = {Taguchi, Y-h.,},
      title = {Unsupervised feature extraction applied to bioinformatics  : a PCA based and TD based approach /},
      publisher = {Springer,},
      address = {Cham :},
      pages = {1 online resource (329 pages)},
      year = {2020},
      abstract = {This book proposes applications of tensor decomposition to  unsupervised feature extraction and feature selection. The  author posits that although supervised methods including  deep learning have become popular, unsupervised methods  have their own advantages. He argues that this is the case  because unsupervised methods are easy to learn since tensor  decomposition is a conventional linear methodology. This  book starts from very basic linear algebra and reaches the  cutting edge methodologies applied to difficult situations  when there are many features (variables) while only small  number of samples are available. The author includes  advanced descriptions about tensor decomposition including  Tucker decomposition using high order singular value  decomposition as well as higher order orthogonal iteration,  and train tenor decomposition. The author concludes by  showing unsupervised methods and their application to a  wide range of topics. Allows readers to analyze data sets  with small samples and many features; Provides a fast  algorithm, based upon linear algebra, to analyze big data;  Includes several applications to multi-view data analyses,  with a focus on bioinformatics.},
      url = {http://library.usi.edu/record/922439},
      doi = {https://doi.org/10.1007/978-3-030-22456-1},
}