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Projection spectral analysis: A unified approach to PCA and ICA with incremental learning.
- Source :
- ETRI Journal; Oct2018, Vol. 40 Issue 5, p634-642, 9p
- Publication Year :
- 2018
-
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 sumâproduct 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 12256463
- Volume :
- 40
- Issue :
- 5
- Database :
- Supplemental Index
- Journal :
- ETRI Journal
- Publication Type :
- Academic Journal
- Accession number :
- 132113919
- Full Text :
- https://doi.org/10.4218/etrij.2017-0304