Canonical correlation analysis (CCA) can be used to find correlating projections of two datasets with co-occurring samples. Instead of correlation, we would typically want to find more general dependencies, measured by mutual information. Variants of CCA based on non-parametric estimation of mutual information have been proposed previously; they outperform traditional CCA for non-Gaussian data but require infeasible amounts of computation for already quite modest sample sizes. We introduce a novel variant that uses a semi parametric estimate leading to a considerably faster algorithm. We apply the method on searching for statistical dependencies between multi-sensory stimuli and functional magnetic resonance imaging (fMRI) of brain activity- in contrast to using regression on either of them.

We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, a prototype application, in which the users try to find interesting scientific articles by browsing their titles. Implicit feedback is inferred from eye movement signals, with discriminative hidden Markov models estimated from existing data in which explicit relevance feedback is available. Collaborative filtering is carried out using the User Rating Profile model, a state-of-the-art probabilistic latent variable model, computed using Markov Chain Monte Carlo techniques. For new document titles the prediction accuracy with eye movements, collaborative filtering, and their combination was significantly better than by chance. The best prediction accuracy still leaves room for improvement but shows that proactive information retrieval and combination of many sources of relevance feedback is feasible.

We model dependencies between m multivariate continuous-valued information sources by a combination of (i) a generalized canonical correlations analysis (gCCA) to reduce dimensionality while preserving dependencies in m - 1 of them, and (ii) summarizing dependencies with the remaining one by associative clustering. This new combination of methods avoids multiway associative clustering which would require a multiway contingency table and hence suffer from curse of dimensionality of the table. The method is applied to summarizing properties of yeast stress by searching for dependencies (commonalities) between expression of genes of baker's yeast Saccharomyces cerevisiae in various stressful treatments, and summarizing stress regulation by finally adding data about transcription factor binding sites.

This work is part of a proactive information retrieval project that aims at estimating relevance from implicit user feedback. The noisy feedback signal needs to be complemented with all available information, and textual content is one of the natural sources. Here we take the first steps by investigating whether this source is at all useful in the challenging setting of estimating the relevance of a new document based on only few samples with known relevance. It turns out that even sophisticated unsupervised methods like multinomial PCA (or latent Dirichlet allocation) cannot help much. By contrast, feature extraction supervised by relevant auxiliary data may help.