Joint Support Vector Machine with Constrained Nonnegative Matrix Factorization and Its Applications

• Main Authors: Mai Lam, 林梅
• Other Authors: Wang Jia-Ching
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/288chh

碩士 === 國立中央大學 === 資訊工程學系 === 105 === The purpose of this study is to investigate the effects of merging maximum margin classification constraints on the constrained non-negative matrix factorization objective function. Non-negative matrix factorization (NMF) is a dimension-reduction technique based on a low-rank approximation of the feature space. Unfortunately, most existing NMF based methods are not ready for encoding higher-order data information and ignore the local geometric structure contained in the data set. Furthermore, the previous classification approaches which the classification and matrix factorization steps are separated independently. The first one performs data transformation and the second one classifies the transformed data using classification methods as support vector machine (SVM). In this research, therefore, we joint SVM and constrained NMF into one by uniting maximum margin classification constraints into the constrained NMF optimization. The proposed algorithm is derived from NMF algorithm by exploiting both spatial and graph-preserving properties. A multiplicative updating algorithm is also proposed to solve the corresponding optimization problem. Experimental results on benchmark image data sets demonstrate the effectiveness of the proposed method. The results show that our proposed algorithm provides better facial representations and achieves higher recognition rates than standard non-negative matrix factorization and its variants.