Paper Info
Title
Adaptive Locality Preserving Regression
Abstract
This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional Frobenius norm' with the special l2,1 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owning to the row-sparsity property of the l2,1 norm. Extensive experiments conducted on the synthetic database with manifold structure and many realworld databases prove the effectiveness of the proposed method. IEEE
Year
Venue
Field
2020
IEEE Transactions on Circuits and Systems for Video Technology
Interpretability,Locality,Regression,Pattern recognition,Feature selection,Computer science,Feature extraction,Matrix norm,Artificial intelligence,Overfitting,Discriminative model
DocType
Citations 
PageRank 
Journal
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Wen Jie128423.38
Zhong Zuofeng2624.56
Zheng Zhang354940.45
Lunke Fei441930.97
Zhihui Lai5120476.03
Chen Runze692.56