Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

February 2018, Vol. 30, No. 2, Pages 505-525
(doi: 10.1162/neco_a_01038)
© 2018 Massachusetts Institute of Technology
Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification
Article PDF (1.51 MB)
Abstract
While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint -norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles—enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.