Transformation of Non-Euclidean Space to Euclidean Space for Efficient Learning of Singular Vectors
Singular value decomposition (SVD) is a popular technique to extract essential information by reducing the dimension of a feature set. SVD is able to analyze a vast matrix in spite of a relatively low computational cost. However, singular vectors produced by SVD have been seldom used in convolutiona...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9137281/ |