Sparse methods for automatic relevance determination

Sparse Coding and Automatic Relevance Determination for Multi-way models

Multi-way modeling has become an important tool in the analysis of large scale multi-modal data. An important class of multi-way models is given by the Tucker model which decomposes the data into components pertaining to each modality as well as a core array indicating how the components of the various modalities interact. Unfortunately, the Tucker model is not unique. Furthermore, establishing...

Automatic Relevance Determination in Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) has become a popular technique for data analysis and dimensionality reduction. However, it is often assumed that the number of latent dimensions (or components) is given. In practice, one must choose a suitable value depending on the data and/or setting. In this paper, we address this important issue by using a Bayesian approach to estimate the latent dime...

A New View of Automatic Relevance Determination

Automatic relevance determination (ARD) and the closely-related sparse Bayesian learning (SBL) framework are effective tools for pruning large numbers of irrelevant features leading to a sparse explanatory subset. However, popular update rules used for ARD are either difficult to extend to more general problems of interest or are characterized by non-ideal convergence properties. Moreover, it r...

Ayesian Automatic Relevance Determination Algorithms for Classifying Gene Expression Data

MOTIVATION We investigate two new Bayesian classification algorithms incorporating feature selection. These algorithms are applied to the classification of gene expression data derived from cDNA microarrays. RESULTS We demonstrate the effectiveness of the algorithms on three gene expression datasets for cancer, showing they compare well with alternative kernel-based techniques. By automatical...

Automatic relevance determination for Least Squares Support Vector Machines classifiers