Latent Constrained Correlation Filter.

Correlation filters are special classifiers designed for shift-invariant object recognition, which are robust to pattern distortions. The recent literature shows that combining a set of sub-filters trained based on a single or a small group of images obtains the best performance. The idea is equivalent to estimating variable distribution based on the data sampling (bagging), which can be interpreted as finding solutions (variable distribution approximation) directly from sampled data space.

Traveling on discrete embeddings of gene expression.

Objective: High-throughput technologies have generated an unprecedented amount of high-dimensional gene expression data. Algorithmic approaches could be extremely useful to distill information and derive compact interpretable representations of the statistical patterns present in the data. This paper proposes a mining approach to extract an informative representation of gene expression profiles based on a generative model called the Counting Grid (CG).

Capturing Spatial Interdependence in Image Features: The Counting Grid, an Epitomic Representation for Bags of Features.

In recent scene recognition research images or large image regions are often represented as disorganized "bags" of features which can then be analyzed using models originally developed to capture co-variation of word counts in text. However, image feature counts are likely to be constrained in different ways than word counts in text. For example, as a camera pans upwards from a building entrance over its first few floors and then further up into the sky Fig.

Mapping brains on grids of features for schizophrenia analysis.

This paper exploits the embedding provided by the counting grid model and proposes a framework for the classification and the analysis of brain MRI images. Each brain, encoded by a count of local features, is mapped into a window on a grid of feature distributions. Similar sample are mapped in close proximity on the grid and their commonalities in their feature distributions are reflected in the overlap of windows on the grid.

Bags of words models of epitope sets: HIV viral load regression with counting grids.

The immune system gathers evidence of the execution of various molecular processes, both foreign and the cells' own, as time- and space-varying sets of epitopes, small linear or conformational segments of the proteins involved in these processes. Epitopes do not have any obvious ordering in this scheme: The immune system simply sees these epitope sets as disordered "bags" of simple signatures based on whose contents the actions need to be decided. The immense landscape of possible bags of epitopes is shaped by the cellular pathways in various cells, as well as the characteristics of the internal sampling process that chooses and brings epitopes to cellular surface.

Investigating topic models' capabilities in expression microarray data classification.

In recent years a particular class of probabilistic graphical models-called topic models-has proven to represent an useful and interpretable tool for understanding and mining microarray data. In this context, such models have been almost only applied in the clustering scenario, whereas the classification task has been disregarded by researchers. In this paper, we thoroughly investigate the use of topic models for classification of microarray data, starting from ideas proposed in other fields (e.

Free Energy Score Spaces: Using Generative Information in Discriminative Classifiers.

A score function induced by a generative model of the data can provide a feature vector of a fixed dimension for each data sample. Data samples themselves may be of differing lengths (e.g.

Fully non-homogeneous hidden Markov model double net: a generative model for haplotype reconstruction and block discovery.

Objective: In the last decade, haplotype reconstruction in unrelated individuals and haplotype block discovery have riveted the attention of computer scientists due to the involved strong computational aspects. Such tasks are usually addressed separately, but recently, statistical techniques have permitted them to be solved jointly. Following this trend we propose a generative model that permits researchers to solve the two problems jointly.