Multiclass heterogeneous domain adaptation via bidirectional ECOC projection.

Heterogeneous domain adaptation aims to exploit the source domain data to train a prediction model for the target domain with different input feature space. Current methods either map the data points from different domains with different feature space to a common latent subspace or use asymmetric projections for learning the classifier. However, these learning methods separate common space learning and shared classifier training. This may lead complex model structure and more parameters to be determined. To appropriately address this problem, we propose a novel bidirectional ECOC projection method, named HDA-ECOC, for heterogeneous domain adaptation. The proposed method projects the inputs and outputs (labels) of two domains into a common ECOC coding space, such that, the common space learning and the shared classifier training can be performed simultaneously. Then, classification of the target testing sample can be directly addressed by an ECOC decoding. Moreover, the unlabeled target data is exploited by estimating the two domains projected instances consistency through a maximum mean discrepancy (MMD) criterion. We formulate this method as a dual convex minimization problem and propose an alternating optimization algorithm for solving it. For performance evaluation, experiments are performed on cross-lingual text classification and cross-domain digital image classification with heterogeneous feature space. The experimental results demonstrate that the proposed method is effective and efficient in solving the heterogeneous domain adaptation problems.