A Feature and Parameter Selection Approach for Visual Domain Adaptation using Particle Swarm Optimization
Ravi Ranjan Prasad Karn, Rakesh Kumar Sanodiya, Twinkle Sharma, Shreshtha Sharan, Kritika Garg, Jimson Mathew, Leehter Yao · 2022 IEEE Congress on Evolutionary Computation (CEC) · 2022
To train a classifier on a specific domain, often called the target domain, we need labeled data. However, there might be non availability of the labeled data in this domain. In this scenario, we look for a related domain called the source domain, where availability of labeled data is abundant in number. The lack of availability of labeled data in the target domain poses a serious problem and several domain adaptation (DA) approaches have been put forward to cope up with this problem. Existing DA methods seek a subspace common between both the domains (source and target domains) where the distribution difference is minimal and perform manual parameter sensitivity tests to find apposite value of each parameter for their respective objective function. However, for distorted original data, obtaining a common subspace is a challenging task and condensing manual parameter sensitivity testing is also costly and a time-intensive process. To overcome these challenges, some DA methods consider particle swarm optimization (PSO) technique. However, none of the existing DA methods simultaneously tackle these challenges. Therefore, in this paper, we put forward a method called Feature and Parameter Selection approach for visual Domain Adaptation (FPSDA) to address these challenges. In FPSDA, a suitable subset of features across both the domains and an apposite value of each parameter are simultaneously chosen using a PSO approach. Moreover, to guide the PSO, the objective functions of Joint Geometrical and Statistical Alignment (JGSA) [1] method along with preserving original similarity of data is considered as an objective function for our proposed approach. Full Scale experiments on benchmark datasets for cross-domain adaptation verify that FPSDA performs better than many state-of-the-art classic machine learning and domain adaptation approaches.