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Incremental Supervised Subspace Learning for Face Recognition
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作者 赵海涛 敬忠良 孙韶媛 《Journal of Shanghai Jiaotong university(Science)》 EI 2007年第6期695-699,共5页
Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of desi... Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of designing an incremental solution of the eigen decomposition on the product of matrices, there is little work for computing LDA incrementally. To avoid this limitation, an incremental supervised subspace learning (ISSL) algorithm was proposed, which incrementally learns an adaptive subspace by optimizing the maximum margin criterion (MMC). With the dynamically added face images, ISSL can effectively constrain the computational cost. Feasibility of the new algorithm has been successfully tested on different face data sets. 展开更多
关键词 INCREMENTAL linear DISCRIMINANT analysis (LDA) FACE recognition FEATURE extraction
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