UDSFS: Unsupervised deep sparse feature selection

详细信息    查看全文

• 作者：Yang Cong^a ; ^{congyang81@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Shuai Wang^a ; ^b ; ^{shuaiwang@sia.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Baojie Fan^c ; ^{jobfbj@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yunsheng Yang^d ; ^{sunny301ddc@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Haibin Yu^a ; ^{yhb@sia.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Deep sparse ; Feature selection ; Machine learning ; Group sparsity ; Computer aided diagnosis
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 July 2016
• 年：2016
• 卷：196
• 期：Complete
• 页码：150-158
• 全文大小：1003 K

文摘

In this paper, we focus on unsupervised feature selection. As we have known, the combination of several feature units into a whole feature vector is broadly adopted for effective object representation, which may inevitably includes some irrelevant/redundant feature units or feature dimensions. Most of the traditional feature selection models can only select the feature dimensions without concerning the intrinsic relationship among different feature units. By taking into consideration the group sparsity of feature dimensions and feature units based on an ℓ_2,1${\ell }_{2,1}$ minimization, we propose a new unsupervised feature selection model, unsupervised deep sparse feature selection (UDSFS) in this paper. In comparison with the state-of-the-arts, our UDSFS model can not only select the most discriminative feature units but also assign proper weight to the useful feature dimensions concurrently; moreover, the efficiency and robustness of our UDSFS can be also improved without extracting the discarded irrelevant feature units. For model optimization, we introduce an efficient iterative algorithm to solve the non-smooth, convex model and obtain a global optimization with the convergence rate as O(1/K²)$O(1/K^2)$ (K is the iteration number). For the experiments, a new medical endoscopic image dataset, Abnormal Endoscopic Image Detection dataset (AEID), is built for evaluation; we also test our model using two public UCI datasets. Various experiments and comparisons with other state-of-the-arts justified the effectiveness and efficiency of our UDSFS model.