1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Generalization of Principal Component Analysis

      Preprint
      ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components. We present a gradient ascent algorithm to solve the problem. For the kernel version of generalized PCA, we show that the solutions can be obtained as fixed points of a simple single-layer recurrent neural network. We also evaluate our algorithms on different datasets.

          Related collections

          Author and article information

          Journal
          29 October 2019
          Article
          1910.13511
          74100fbb-a118-4e5a-8466-9f61e2ea756f

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          cs.LG eess.SP stat.ML

          Machine learning,Artificial intelligence,Electrical engineering
          Machine learning, Artificial intelligence, Electrical engineering

          Comments

          Comment on this article