Rank-κ 2-D multinomial logistic regression for matrix data classification

Kun Song, Feiping Nie, Junwei Han, Xuelong Li

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9 引用 (Scopus)

摘要

The amount of matrix data has increased rapidly nowadays. How to classify matrix data efficiently is an important issue. In this paper, by discovering the shortages of 2-D linear discriminant analysis and 2-D logistic regression, a novel 2-D framework named rank- κ 2-D multinomial logistic regression (2DMLR-RK) is proposed. The 2DMLR-RK is designed for a multiclass matrix classification problem. In the proposed framework, each category is modeled by a left projection matrix and a right projection matrix with rank κ. The left projection matrices capture the row information of matrix data, and the right projection matrices acquire the column information. We identify the parameter κ plays the role of balancing the capacity of learning and generalization of the 2DMLR-RK. In addition, we develop an effective framework for solving the proposed nonconvex optimization problem. The convergence, initialization, and computational complexity are discussed. Extensive experiments on various types of data sets are conducted. Comparing with 1-D methods, 2DMLR-RK not only achieves a better classification accuracy, but also costs less computation time. Comparing with other state-of-the-art 2-D methods, the 2DMLR-RK achieves a better performance for matrix data classification.

源语言英语
页(从-至)3524-3537
页数14
期刊IEEE Transactions on Neural Networks and Learning Systems
29
8
DOI
出版状态已出版 - 8月 2018

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