Information-Theoretic Domain Adaptation Under Severe Noise Conditions
Keywords:domain adaptation, information-theoretic learning, correntropy
Cross-domain data reconstruction methods derive a shared transformation across source and target domains. These methods usually make a specific assumption on noise, which exhibits limited ability when the target data are contaminated by different kinds of complex noise in practice. To enhance the robustness of domain adaptation under severe noise conditions, this paper proposes a novel reconstruction based algorithm in an information-theoretic setting. Specifically, benefiting from the theoretical property of correntropy, the proposed algorithm is distinguished with: detecting the contaminated target samples without making any specific assumption on noise; greatly suppressing the negative influence of noise on cross-domain transformation. Moreover, a relative entropy based regularization of the transformation is incorporated to avoid trivial solutions with the reaped theoretic advantages, i.e., non-negativity and scale-invariance. For optimization, a half-quadratic technique is developed to minimize the non-convex information-theoretic objectives with explicitly guaranteed convergence. Experiments on two real-world domain adaptation tasks demonstrate the superiority of our method.