Non-negativity and locality constrained Laplacian sparse coding for image classification

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• 作者：Ying Shi^a ; ^{1329990417@qq.com} ; Yuan Wan^a ; ^{wanyuan@whut.edu.cn} ; Kefeng Wu^b ; ^{787145813@qq.com} ; Xiaoli Chen^a ; ^{785535947@qq.com}
• 关键词：Non-negativity ; Locality ; LSC ; K nearest neighbourhoods ; SPD ; MP
• 刊名：Expert Systems with Applications
• 出版年：2017
• 出版时间：15 April 2017
• 年：2017
• 卷：72
• 期：Complete
• 页码：121-129
• 全文大小：1227 K
• 卷排序：72

文摘

Non-negativity for dictionary and codes is proposed to avoid features offsetting each other. Locality for sparse codes is added, which preserves local information for features. Laplacian regularization is used to preserve the consistency of codes with similar features. Our proposed method has better performance than previous algorithms.