Efficient Estimation of Unique Components in Independent Component Analysis by Matrix Representation

Yoshitatsu Matsuda, Kazunori Yamaguch
8/30/2024
cs.LGcs.NE

No official code found. Best implementation: yoshitatsumatsuda/orderingICA (0 stars, last commit 2024-09-02).

Abstract

Independent component analysis (ICA) is a widely used method in various applications of signal processing and feature extraction. It extends principal component analysis (PCA) and can extract important and complicated components with small variances. One of the major problems of ICA is that the uniqueness of the solution is not guaranteed, unlike PCA. That is because there are many local optima in optimizing the objective function of ICA. It has been shown previously that the unique global optimum of ICA can be estimated from many random initializations by handcrafted thread computation. In this paper, the unique estimation of ICA is highly accelerated by reformulating the algorithm in matrix representation and reducing redundant calculations. Experimental results on artificial datasets and EEG data verified the efficiency of the proposed method.

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Code Implementations(1)

yoshitatsumatsuda/orderingICACommunity100%
00MATLABApr 17, 20182 years ago

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Cite this paper

@article{matsuda2024efficient,
  title  = {Efficient Estimation of Unique Components in Independent Component Analysis by Matrix Representation},
  author = {Yoshitatsu Matsuda and Kazunori Yamaguch},
  year   = {2024},
  eprint = {2408.17118},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.17118v2}
}

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