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Recently there has been increasing interest in the problem of transfer learning, in which the typical assumption that training and testing data are drawn from identical distributions is relaxed. We specifically address the problem of transductive transfer learning in which we have access to labeled training data and unlabeled testing data potentially drawn(More)
Effectively utilizing readily available auxiliary data to improve predictive performance on new modeling tasks is a key problem in data mining. In this research, the goal is to transfer knowledge between sources of data, particularly when ground-truth information for the new modeling task is scarce or is expensive to collect where leveraging any auxiliary(More)
We consider the problem of Object Safety: how objects endowed with processing, communication, and sensing capabilities can determine their safety. We assign an agent to each object capable of looking out for its own self interests , while concurrently collaborating with its neighbors and learning/reinforcing its beliefs from them. After considering related(More)
—There has been increasing interest in incorporating sensing systems into objects or the environment for monitoring purposes. In this work we compare approaches to performing fully-distributed anomaly detection as a means of detecting security threats for objects equipped with sensing and communication abilities. With the desirability of increased(More)
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, for many real world problems, we have such prior knowledge about structure relationships among features. For instance, in Microarray analysis where the genes are features, the(More)
High dimensional data challenges current feature selection methods. For many real world problems we often have prior knowledge about the relationship of features. For example in microarray data analysis, genes from the same biological pathways are expected to have similar relationship to the outcome that we target to predict. Recent regularization methods(More)
Multi-view semi-supervised learning methods try to exploit the combination of multiple views along with large amounts of unlabeled data in order to learn better predictive functions when limited labeled data is available. However, lack of complete view data limits the applicability of multi-view semi-supervised learning to real world data. Commonly, one(More)
Classification using microarray gene expression data is an important task in bioinformatics. Due to the high dimensionality and small sample size that characterizes microarray data, there has recently been a drive to incorporate any available information in addition to the expression data in the classification process. As a result, much work has begun on(More)