Data induced masking representation learning for face data analysis

Abstract

Representation learning models, such as the sparse representation and low-rank representation, have shown pleasing efficacy in exploring the intrinsic data structures for patternrecognition tasks. However, conventional methods ignore the local geometric and similarity information among samples, and the performance is restricted. To address this issue, this paper proposes a novel Data Induced Masking Representation (DIMR) learning model by imposing explicit regularization and low-rank constraint. Specifically, DIMR is formulated for shrinking the representations of inter-class and non-neighbor samples. An extra representation regularization term is deployed with a data induced mask matrix, which can incorporate label and locality priors to guide the learning of affinity representation matrix. The affinity graph derived from DIMR is with low-rank, locality preservation (sparsity) and label guiding, such that it can better characterize the adjacent relationship between samples. Extensive experiments on benchmark face datasets demonstrate the superiority of DIMR for both semi-supervised classification and semi-supervised subspace learning tasks.