Paper Info
Title
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints.
Abstract
In this paper, we aim at learning simultaneously a discriminative dictionary and a robust projection matrix from noisy data. The joint learning, makes the learned projection and dictionary a better fit for each other, so a more accurate classification can be obtained. However, current prevailing joint dimensionality reduction and dictionary learning methods, would fail when the training samples are noisy or heavily corrupted. To address this issue, we propose a joint projection and dictionary learning using low-rank regularization and graph constraints (JPDL-LR). Specifically, the discrimination of the dictionary is achieved by imposing Fisher criterion on the coding coefficients. In addition, our method explicitly encodes the local structure of data by incorporating a graph regularization term, that further improves the discriminative ability of the projection matrix. Inspired by recent advances of low-rank representation for removing outliers and noise, we enforce a low-rank constraint on sub-dictionaries of all classes to make them more compact and robust to noise. Experimental results on several benchmark datasets verify the effectiveness and robustness of our method for both dimensionality reduction and image classification, especially when the data contains considerable noise or variations.
Year
Venue
Field
2016
arXiv: Computer Vision and Pattern Recognition
Dimensionality reduction,Pattern recognition,K-SVD,Computer science,Outlier,Projection (linear algebra),Robustness (computer science),Regularization (mathematics),Artificial intelligence,Contextual image classification,Discriminative model,Machine learning
DocType
Volume
Citations 
Journal
abs/1603.07697
0
PageRank 
References 
Authors
0.34
15
3
Name
Order
Citations
PageRank
Homa Foroughi1313.65
Ray Nilanjan254155.39
Hong Zhang358274.33