Latent space domain transfer between high dimensional overlapping distributions

Transferring knowledge from one domain to another is challenging due to a number of reasons. Since both conditional and marginal distribution of the training data and test data are non-identical, model trained in one domain, when directly applied to a different domain, is usually low in accuracy. For many applications with large feature sets, such as text document, sequence data, medical data, image data of different resolutions, etc. two domains usually do not contain exactly the same features, thus introducing large numbers of "missing values" when considered over the union of features from both domains. In other words, its marginal distributions are at most overlapping. In the same time, these problems are usually high dimensional, such as, several thousands of features. Thus, the combination of high dimensionality and missing values make the relationship in conditional probabilities between two domains hard to measure and model. To address these challenges, we propose a ...

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Sihong Xie, Wei Fan, Jing Peng, Olivier Verscheure, Jiangtao Ren
• Keywords: Internet Technology, Marginal Distributions, Related Domains, Related Labeled Instances, WWW 2009