Transfer Learning via Dimensionality Reduction

Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the training and testing problems have different distributions or features. In this paper, we consider transfer learning via dimensionality reduction. To solve this problem, we learn a low-dimensional latent feature space where the distributions between the source domain data and the target domain data are the same or close to each other. Onto this latent feature space, we project the data in related domains where we can apply standard learning algorithms to train classification or regression models. Thus, the latent feature space can be treated as a bridge of transferring knowledge from the source domain to the target domain. The main contribution of our work is that we propose a new dimensionality reduction method to find a latent space, which minimizes the distance between distributions of the data in different dom...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Sinno Jialin Pan, James T. Kwok, Qiang Yang
• Keywords: AAAI 2008, Intelligent Agents, Latent Feature Space, Source Domain, Target Domain