Multilinear Subspace Learning

Multilinear Subspace Learning

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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Multilinear subspace learning is a dimensionality reduction approach for finding a low-dimensional representation of high-dimensional tensor data through direct mapping, without going through vectorization. The term tensor in MSL refers to multidimensional arrays. Examples of tensor data include images, video sequences, and hyperspectral cubes. The mapping from a high-dimensional tensor space to a low-dimensional tensor space or vector space is named as multilinear projection. MSL methods are higher-order generalizations of linear subspace learning methods such as principal component analysis and linear discriminant analysis. In the literature, MSL is also referred to as tensor subspace learning or tensor subspace analysis. Research on MSL has progressed from heuristic exploration in 2000s to systematic investigation in 2010s.
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Product details

  • Paperback | 56 pages
  • 152 x 229 x 3mm | 95g
  • Chrono Press
  • United States
  • English
  • 6136523752
  • 9786136523750