Adaptive hybrid representation for graph-based semi-supervised classification

Abstract

Building an informative graph over a collection of images or signals is one of the most important tasks in semi-supervised learning (SSL). Local Hybrid Coding (LHC) was recently proposed as an alternative to the sparse coding scheme that is used in Sparse Representation Classifier (SRC). The LHC blends sparsity and bases-locality criteria in a unified optimization problem. This paper introduces a data-driven graph construction method that exploits and extends the LHC scheme. We propose a new coding scheme coined Adaptive Local Hybrid Coding (ALHC). The main contributions are as follows. First, the proposed coding scheme automatically selects the local and non-local bases of LHC using data similarities calculated by Locality-constrained Linear code. Second, the estimated similarities are used in the regularization of the final solution. Third, the proposed ALHC scheme is used in order to construct graphs over image datasets. For SSL tasks adopting label propagation, we show that the proposed graph outperforms many state-of-the art graphs on three public face datasets.