Class-Specific Feature Selection With Local Geometric Structure and Discriminative Information Based on Sparse Similar Samples

2015 ◽  
Vol 12 (7) ◽  
pp. 1392-1396 ◽  
Author(s):  
Xi Chen ◽  
Yanfeng Gu
2022 ◽  
Vol 12 (1) ◽  
pp. 80
Author(s):  
Zhuqing Jiao ◽  
Siwei Chen ◽  
Haifeng Shi ◽  
Jia Xu

Feature selection for multiple types of data has been widely applied in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) classification research. Combining multi-modal data for classification can better realize the complementarity of valuable information. In order to improve the classification performance of feature selection on multi-modal data, we propose a multi-modal feature selection algorithm using feature correlation and feature structure fusion (FC2FS). First, we construct feature correlation regularization by fusing a similarity matrix between multi-modal feature nodes. Then, based on manifold learning, we employ feature matrix fusion to construct feature structure regularization, and learn the local geometric structure of the feature nodes. Finally, the two regularizations are embedded in a multi-task learning model that introduces low-rank constraint, the multi-modal features are selected, and the final features are linearly fused and input into a support vector machine (SVM) for classification. Different controlled experiments were set to verify the validity of the proposed method, which was applied to MCI and AD classification. The accuracy of normal controls versus Alzheimer’s disease, normal controls versus late mild cognitive impairment, normal controls versus early mild cognitive impairment, and early mild cognitive impairment versus late mild cognitive impairment achieve 91.85 ± 1.42%, 85.33 ± 2.22%, 78.29 ± 2.20%, and 77.67 ± 1.65%, respectively. This method makes up for the shortcomings of the traditional multi-modal feature selection based on subjects and fully considers the relationship between feature nodes and the local geometric structure of feature space. Our study not only enhances the interpretation of feature selection but also improves the classification performance, which has certain reference values for the identification of MCI and AD.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 133017-133029
Author(s):  
Dongsheng Bi ◽  
Rui Yu ◽  
Mengya Li ◽  
Yang Yang ◽  
Kun Yang ◽  
...  

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ao Li ◽  
Hayaru Shouno

We proposed an efficient image denoising scheme by fused lasso with dictionary learning. The scheme has two important contributions. The first one is that we learned the patch-based adaptive dictionary by principal component analysis (PCA) with clustering the image into many subsets, which can better preserve the local geometric structure. The second one is that we coded the patches in each subset by fused lasso with the clustering learned dictionary and proposed an iterative Split Bregman to solve it rapidly. We present the capabilities with several experiments. The results show that the proposed scheme is competitive to some excellent denoising algorithms.


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