Locality preserving multi-nominal logistic regression

In this paper, we propose a novel algorithm of multi-nominal logistic regression in which the locality regularization term is introduced. The locality is defined by the neighborhood information of the data set and is preserved in the mapped feature space. By using the standard benchmark datasets, it was shown that the proposed algorithm gave higher recognition rates than the linear SVM in binary classification problems. The recognition rates for multi-class classification problem were also better than the general multi-nominal logistic regression.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Kenji Watanabe, Takio Kurita
• Keywords: Computer Vision, ICPR 2008, Locality Regularization Term, Multi-nominal Logistic Regression, Recognition Rates