Abstract
It is often desirable to calculate only a few terms of the SVD expansion of a matrix, corresponding to the largest or smallest singular values. Two algorithms, based on gradient and conjugate gradient search, are proposed for this purpose. SVD is computed term by term in a decreasing or increasing order of singular values. The algorithms are simple to implement and are especially advantageous with large matrices.
Original language | English |
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Pages (from-to) | 137-142 |
Number of pages | 6 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | PAMI-9 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 1987 |
Keywords
- Conjugate gradient
- Rayleigh quotient
- gradient search
- partial singular
- value decomposition
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics