Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning

TL;DR

This paper introduces a novel multiple kernel sparse coding framework that enhances visual recognition by integrating diverse descriptors into a unified space, optimizing class discrimination through graph-based kernel weighting.

Contribution

It proposes a new method for sparse coding and dictionary learning in multiple kernel spaces with adaptive kernel weighting for improved supervised and unsupervised learning.

Findings

Outperforms existing sparse coding methods in object recognition.

Achieves superior clustering results compared to state-of-the-art techniques.

Demonstrates robustness across different visual recognition tasks.

Abstract

In complex visual recognition tasks it is typical to adopt multiple descriptors, that describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1-D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.