A nonparametric Bayesian approach to co-clustering ensembles is presented. Similar to clustering ensembles, co-clustering ensembles combine various base co-clustering results to obtain a more robust consensus co-clustering. To avoid pre-specifying the number of co-clusters, we specify independent Dirichlet process priors for the row and column clusters. Thus, the numbers of row- and column-clusters are unbounded a priori; the actual numbers of clusters can be learned a posteriori from observations. Next, to model non-independence of row- and column-clusters, we employ a Mondrian Process as a prior distribution over partitions of the data matrix. As a result, the co-clusters are not restricted to a regular grid partition, but form nested partitions with varying resolutions. The empirical evaluation demonstrates the effectiveness of nonparametric Bayesian co-clustering ensembles and their advantages over traditional co-clustering methods.

Traditional approaches to document classification requires labeled data in order to construct reliable and accurate classifiers. Unfortunately, labeled data are seldom available, and often too expensive to obtain. Given a learning task for which training data are not available, abundant labeled data may exist for a different but related domain. One would like to use the related labeled data as auxiliary information to accomplish the classification task in the target domain. Recently, the paradigm of transfer learning has been introduced to enable effective learning strategies when auxiliary data obey a different probability distribution. A co-clustering based classification algorithm has been previously proposed to tackle cross-domain text classification. In this work, we extend the idea underlying this approach by making the latent semantic relationship between the two domains explicit. This goal is achieved with the use of Wikipedia. As a result, the pathway that allows to propagate labels between the two domains not only captures common words, but also semantic concepts based on the content of documents. We empirically demonstrate the efficacy of our semantic-based approach to cross-domain classification using a variety of real data.

Document classification presents difficult challenges due to the sparsity and the high dimensionality of text data, and to the complex semantics of the natural language. The traditional document representation is a word-based vector (Bag of Words, or BOW), where each dimension is associated with a term of the dictionary containing all the words that appear in the corpus. Although simple and commonly used, this representation has several limitations. It is essential to embed semantic information and conceptual patterns in order to enhance the prediction capabilities of classification algorithms. In this paper, we overcome the shortages of the BOW approach by embedding background knowledge derived from Wikipedia into a semantic kernel, which is then used to enrich the representation of documents. Our empirical evaluation with real data sets demonstrates that our approach successfully achieves improved classification accuracy with respect to the BOW technique, and to other recently developed methods.