Abstract—Dataset shift from the training data in a source domain to the data in a target domain poses a great challenge for many statistical learning methods. Most algorithms can be viewed as exploiting only the first-order statistics, namely, the empirical mean discrepancy to evaluate the distribution gap. Intuitively, considering only the empirical mean may not be statistically efficient. In this paper, we propose a nonparametric distance metric with a good property which jointly considers the empirical mean (Location) and sample covariance (Scatter) difference. More specifically, we propose an improved symmetric Stein’s loss function which combines the mean and covariance discrepancy into a unified Bregman matrix divergence of which Jensen-Shannon divergence between normal distributions is a particular case. Our target is to find a good feature representation which can reduce the distribution gap between different domains, at the same time, ensure that the new derived representation can encode most discriminative components with respect to the label information. We have conducted extensive experiments on several document classification datasets to demonstrate the effectiveness of our proposed method. Keywords-Domain Adaptation, Feature Extraction I.

Abstract – In this paper, a transfer learning case study on cotton quality evaluation is presented whereby a new prototype imaging system (target problem) is trained using knowledge transferred from a reference system (source problem). We describe the properties of both systems and explain how our problem setup is a specific case of inductive transfer learning. We then present a feature-based domain adaptation framework to reduce domain divergence, allowing data to be compared between the two systems. Finally, we discuss ways of transferring domain-specific knowledge from the reference system to the new system. We demonstrate our approach on cotton data available from the reference system, and those that we generate using our prototype system.

Accurate prediction of user behaviors is important for many social media applications, including social marketing, personalization and recommendation, etc. A major challenge lies in that, the available behavior data or interactions between users and items in a given social network are usually very limited and sparse (e.g., ≥ 99.9 % empty). Many previous works model user behavior from only historical user logs. We observe that many people are members of several social networks in the same time, such as Facebook, Twitter and Tencent’s QQ 1. Importantly, their behaviors and interests in different networks influence one another. This gives us an opportunity to leverage the knowledge of user behaviors in different networks, in order to alleviate the data sparsity problem, and enhance the predictive performance of user modeling. Combining differentnetworks“simply and naively”does not work well. Instead, we formulate the problem to model multiple networks as“composite network knowledge transfer”. We first select the most suitable networks inside a composite social network via a hierarchical Bayesian model, parameterized for individual users, and then build topic models for user behavior prediction using both the relationships in the selected networks and related behavior data. To handle big data, we have implemented the algorithm using Map/Reduce. We demonstrate that the proposed composite network-based user behavior model significantly improve the predictive accuracy over a number of existing approaches on several real world applications, such as a very large social-networking dataset from Tencent Inc.