document: 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 framework that first brings the marginal distributions of two domains closer by `` filling up '' those missing values of disjoint features . Afterwards , it looks for those comparable sub-structures in the `` latent-space '' as mapped from the expanded feature vector , where both marginal and conditional distribution are similar . With these sub-structures in latent space , the proposed approach then find common concepts that are transferable across domains with high probability . During prediction , unlabeled instances are treated as `` queries '' , the mostly related labeled instances from out-domain are retrieved , and the classification is made by weighted voting using retrieved out-domain examples . We formally show that importing feature values across domains and latent semantic index can jointly make the distributions of two related domains easier to measure than in original feature space , the nearest neighbor method employed to retrieve related out domain examples is bounded in error when predicting in-domain examples . Software and datasets are available for download .
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