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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.17118 |
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| _version_ | 1866908541647847424 |
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| author | Matsuda, Yoshitatsu Yamaguch, Kazunori |
| author_facet | Matsuda, Yoshitatsu Yamaguch, Kazunori |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_17118 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Estimation of Unique Components in Independent Component Analysis by Matrix Representation Matsuda, Yoshitatsu Yamaguch, Kazunori Machine Learning Neural and Evolutionary Computing 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. |
| title | Efficient Estimation of Unique Components in Independent Component Analysis by Matrix Representation |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2408.17118 |