Projection spectral analysis: A unified approach to PCA and ICA with incremental learning
- Authors
- Kang, Hoon; Lee, Su Hyun
- Issue Date
- Oct-2018
- Publisher
- WILEY
- Keywords
- independent component analysis; machine learning; neural network; principal component analysis; projection spectral analysis; singular value decomposition; spectral theorem
- Citation
- ETRI JOURNAL, v.40, no.5, pp 634 - 642
- Pages
- 9
- Journal Title
- ETRI JOURNAL
- Volume
- 40
- Number
- 5
- Start Page
- 634
- End Page
- 642
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/729
- DOI
- 10.4218/etrij.2017-0304
- ISSN
- 1225-6463
2233-7326
- Abstract
- Projection spectral analysis is investigated and refined in this paper, in order to unify principal component analysis and independent component analysis. Singular value decomposition and spectral theorems are applied to nonsymmetric correlation or covariance matrices with multiplicities or singularities, where projections and nilpotents are obtained. Therefore, the suggested approach not only utilizes a sum-product of orthogonal projection operators and real distinct eigenvalues for squared singular values, but also reduces the dimension of correlation or covariance if there are multiple zero eigenvalues. Moreover, incremental learning strategies of projection spectral analysis are also suggested to improve the performance.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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