Supervised and Projected Sparse Coding for Image Classification

View Researcher II's Other Codes

Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”

Please contact us in case of a broken link from here

Authors Jin Huang, Feiping Nie, Heng Huang, Chris Ding
Journal/Conference Name The Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI)
Paper Category
Paper Abstract Classic sparse representation for classification (SRC) method fails to incorporate the label information of training images, and meanwhile has a poor scalability due to the expensive computation for l1 norm. In this paper, we propose a novel subspace sparse coding method with utilizing label information to effectively classify the images in the subspace. Our new approach unifies the tasks of dimension reduction and supervised sparse vector learning, by simultaneously preserving the data sparse structure and meanwhile seeking the optimal projection direction in the training stage, therefore accelerates the classification process in the test stage. Our method achieves both flat and structured sparsity for the vector representations, therefore making our framework more discriminative during the subspace learning and subsequent classification. The empirical results on 4 benchmark data sets demonstrate the effectiveness of our method.
Date of publication 2013
Code Programming Language MATLAB
Comment

Copyright Researcher II 2021