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Canonical correlation analysis (CCA) is a classical method for seeking correlations between two multivariate data sets. During the last ten years, it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications. We review recent developments in Bayesian models and(More)
We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternative variables collected from statistics(More)
We have earlier introduced a principle for learning metrics, which shows how metric-based methods can be made to focus on discriminative properties of data. The main applications are in supervising unsupervised learning to model interesting variation in data, instead of modeling all variation as plain unsupervised learning does. The metrics are derived by(More)
Query formulation and efficient navigation through data to reach relevant results are undoubtedly major challenges for image or video retrieval. Queries of good quality are typically not available and the search process needs to rely on relevance feedback given by the user, which makes the search process iterative. Giving explicit relevance feedback is(More)
This paper describes Pinview, a content-based image retrieval system that exploits implicit relevance feedback during a search session. The goal is to retrieve interesting images and the relevance feedback could be eye movements or clicks on the images. Pinview contains several novel methods that infer the intent of the user. From relevance feedback and(More)
BACKGROUND Ewing sarcoma family of tumors (ESFT), characterized by t(11;22)(q24;q12), is one of the most common tumors of bone in children and young adults. In addition to EWS/FLI1 gene fusion, copy number changes are known to be significant for the underlying neoplastic development of ESFT and for patient outcome. Our genome-wide high-resolution analysis(More)
We introduce a mixture of probabilistic canonical correlation analyzers model for analyzing local correlations, or more generally mutual statistical dependencies, in cooccurring data pairs. The model extends the traditional canonical correlation analysis and its probabilistic interpretation in three main ways. First, a full Bayesian treatment enables(More)
CMF is a technique for simultaneously learning low-rank representations based on a collection of matrices with shared entities. A typical example is the joint modeling of user-item, item-property, and user-feature matrices in a recommender system. The key idea in CMF is that the embeddings are shared across the matrices, which enables transferring(More)
Multi-view learning studies how several views, different feature representations , of the same objects could be best utilized in learning. In other words, multi-view learning is analysis of co-occurrence data, where the observations are co-occurrences of samples in the views. Standard multi-view learning such as joint density modeling cannot be done in the(More)