• Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents an approach to extract biclusters from expression micro array data using topic models - a class of probabilistic models which allow to detect interpretable groups of highly correlated genes and samples. Starting from a topic model learned from the expression matrix, some automatic rules to extract biclusters are presented, which overcome the drawbacks of previous approaches. The methodology has been positively tested with synthetic benchmarks, as well as with a real experiment involving two different species of grape plants (Vitis vinifera and Vitis riparia).
    20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey, 23-26 August 2010; 01/2010
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Classification of samples in expression microarray experiments represents a crucial task in bioinformatics and biomedicine. In this paper this scenario is addressed by employing a particular class of statistical approaches, called Topic Models. These models, firstly introduced in the text mining community, permit to extract from a set of objects (typically documents) an interpretable and rich description, based on an intermediate representation called topics (or processes). In this paper the expression microarray classification task is cast into this probabilistic context, providing a parallelism with the text mining domain and an interpretation. Two different topic models are investigated, namely the Probabilistic Latent Semantic Analysis (PLSA) and the Latent Dirichlet Allocation (LDA). An experimental evaluation of the proposed methodologies on three standard datasets confirms their effectiveness, also in comparison with other classification methodologies.
    Proceedings of the 2010 ACM Symposium on Applied Computing (SAC), Sierre, Switzerland, March 22-26, 2010; 01/2010
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vectorial data (e.g., support vector machines) are learned discriminatively. A generative embedding is a mapping from the object space into a fixed dimensional score space, induced by a generative model, usually learned from data. The fixed dimensionality of these generative score spaces makes them adequate for discriminative learning of classifiers, thus bringing together the best of the discriminative and generative paradigms. In particular, it was recently shown that this hybrid approach outperforms a classifier obtained directly for the generative model upon which the score space was built. Using a generative embedding involves two steps: (i) defining and learning the generative model and using it to build the embedding; (ii) discriminatively learning a (maybe kernel) classifier on the adopted score space. The literature on generative embeddings is essentially focused on step (i), usually using some standard off-the-shelf tool for step (ii). In this paper, we adopt a different approach, by focusing also on the discriminative learning step. In particular, we combine two very recent and top performing tools in each of the steps: (i) the free energy score space; (ii) non-extensive information theoretic kernels. In this paper, we apply this methodology in scene recognition. Experimental results on two benchmark datasets shows that our approach yields state-of-the-art performance.
    Proceedings of the International Conference on Image Processing, ICIP 2010, September 26-29, Hong Kong, China; 01/2010

Full-text (2 Sources)

View
17 Downloads
Available from
May 30, 2014