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Approximate Sparse Multinomial Logistic Regression for Classification.
- Source :
- IEEE Transactions on Pattern Analysis & Machine Intelligence; Feb2020, Vol. 42 Issue 2, p490-493, 4p
- Publication Year :
- 2020
-
Abstract
- We propose a new learning rule for sparse multinomial logistic regression (SMLR). The new rule is the generalization of the one proposed in the pioneering work by Krishnapuram et al. In our proposed method, the parameters of SMLR are iteratively estimated from log-posterior by using some approximations. The proposed update rule provides a faster convergence compared to the state-of the-art methods used for SMLR parameter estimation. The estimated parameters are tested on the pixel-based classification of hyperspectral images. The experimental results on real hyperspectral images show that the classification accuracy of proposed method is also better than those of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- PARAMETER estimation
CLASSIFICATION
APPROXIMATION algorithms
REGRESSION trees
Subjects
Details
- Language :
- English
- ISSN :
- 01628828
- Volume :
- 42
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Pattern Analysis & Machine Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 141230585
- Full Text :
- https://doi.org/10.1109/TPAMI.2019.2904062