32,446 research outputs found
Collaborative Hierarchical Sparse Modeling
Sparse modeling is a powerful framework for data analysis and processing.
Traditionally, encoding in this framework is done by solving an l_1-regularized
linear regression problem, usually called Lasso. In this work we first combine
the sparsity-inducing property of the Lasso model, at the individual feature
level, with the block-sparsity property of the group Lasso model, where sparse
groups of features are jointly encoded, obtaining a sparsity pattern
hierarchically structured. This results in the hierarchical Lasso, which shows
important practical modeling advantages. We then extend this approach to the
collaborative case, where a set of simultaneously coded signals share the same
sparsity pattern at the higher (group) level but not necessarily at the lower
one. Signals then share the same active groups, or classes, but not necessarily
the same active set. This is very well suited for applications such as source
separation. An efficient optimization procedure, which guarantees convergence
to the global optimum, is developed for these new models. The underlying
presentation of the new framework and optimization approach is complemented
with experimental examples and preliminary theoretical results.Comment: To appear in CISS 201
Improved processing of microarray data using image reconstruction techniques
Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed. By subtracting this background recreation from the original, the gene ratios can be calculated with more precision and less influence from outliers and other artifacts that would normally make the analysis of this data more difficult. The new technique is also beneficial, as it does not rely on the accurate fitting of a region to each gene, with its only requirement being an approximate coordinate. In experiments conducted, the new method was tested against one of the mainstream methods of processing spotted microarray images. Our method is shown to produce much less variation in gene measurements. This evidence is supported by clustering results that show a marked improvement in accuracy
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