Robust multi-model adaptation regression with local feature space representation

Abstract

Learning visual models of object categories generally requires a large number of training examples. We show in this paper that it is possible to uncover much discriminative information about a visual category from a small number of examples with just fewlabeled data. The key insight is that, rather than learning from scratch with the original feature representation, we learn an optimal target model with the local feature space representations by leveraging some prior models pre-trained on other related datasets, no matter how different they might be. This target model can be obtained by learning a robust regression scheme with a multi-source adaptation regularization term, which is formally formulated as a Robust Multi-model Adaptation Regression (RMAR) framework with local feature space representations of the target data. This framework is composed of three core components: (1) local consistency Laplacian regularization by constructing a self-adaptive affinity graph based on local feature space representation (LFSR), which aims to augment the discriminative space of the target domain; (2) robust regression learning scheme with feature selection based on l2,1-norm minimization by explicitly considering the regression noise/outlier as well as the regression residual; and (3) scatter constrained multi-model adaptation regularization for generalizing the established robust regression framework to exploit the prior models, under the assumption that multiple auxiliary discriminative models can help the semi-supervised learning from few labeled samples. Lastly, we further propose an effective extension to RMAR, i.e., ensemble LFSR-graph Laplacians regularization framework. The optimization algorithms for RMAR and its extension can be efficiently solved by the alternating iterative strategy, and the iteration convergence can be theoretically guaranteed. Experiments over several real datasets show the promising performance of our methods compared with several representative state-of-the-art works.