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Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution(More)
Most existing transfer learning techniques are limited to problems of knowledge transfer across tasks sharing the same set of class labels. In this paper, however, we relax this constraint and propose a spectral-based solution that aims at unveiling the intrinsic structure of the data and generating a partition of the target data, by transferring the(More)
Co-clustering was proposed to simultaneously cluster objects and features to explore inter-correlated patterns. For example, by analyzing the blog click-through data, one finds the group of users who are interested in a specific group of blogs in order to perform applications such as recommendations. However, it is usually very difficult to achieve good(More)
In many applications, it is very expensive or time consuming to obtain a lot of labeled examples. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have 1) different feature spaces (e.g., image versus text data), 2) different data distributions, and 3) different output spaces?(More)
RNA interference via exogenous small interference RNAs (siRNA) is a powerful tool in gene function study and disease treatment. Designing efficient and specific siRNA on target gene remains the key issue in RNAi. Although various in silico models have been proposed for rational siRNA design, most of them focus on the efficiencies of selected siRNAs, while(More)
When labeled examples are not readily available, active learning and transfer learning are separate efforts to obtain labeled examples for inductive learning. Active learning asks domain experts to label a small set of examples, but there is a cost incurred for each answer. While transfer learning could borrow labeled examples from a different domain(More)
Lack of labeled training examples is a common problem for many applications. At the same time, there is often an abundance of labeled data from related tasks, although they have different distributions and outputs (e.g., different class labels, and different scales of regression values). In the medical domain, for example, we may have a limited number of(More)
Multiple data sources containing different types of features may be available for a given task. For instance, users' profiles can be used to build recommendation systems. In addition, a model can also use users' historical behaviors and social networks to infer users' interests on related products. We argue that it is desirable to collectively use any(More)