Supervised and Projected Sparse Coding for Image Classification
Year: 2013
Type: conference-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 l_1 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.
Authors: Jin Huang, Feiping Nie, Heng Huang, Chris Ding
Institution: The University of Texas at Arlington
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: National Science Foundation
Awards: Collaborative Research: Non-negative Matrix Factorizations for Data Mining: Foundations, Capabilities, and Applications, Collaborative Research: Matrix-Model Machine Learning: Unifying Machine Learning and Scientific Computing, III: Small: Collaborative Research: A Large-Scale Data Mining Framework for Genome-Wide Mapping of Multi-Modal Phenotypic Biomarkers and Outcome Prediction, New Theoretical Foundations of Tensor Applications: Clustering, Error Analysis, Global Convergence, and Robust Formulations
OpenAlex ID: W602182927