159,098 research outputs found
Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
Background: High-throughput proteomics techniques, such as mass spectrometry
(MS)-based approaches, produce very high-dimensional data-sets. In a clinical
setting one is often interested in how mass spectra differ between patients of
different classes, for example spectra from healthy patients vs. spectra from
patients having a particular disease. Machine learning algorithms are needed to
(a) identify these discriminating features and (b) classify unknown spectra
based on this feature set. Since the acquired data is usually noisy, the
algorithms should be robust against noise and outliers, while the identified
feature set should be as small as possible.
Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based
on the theory of compressed sensing that allows us to identify a minimal
discriminating set of features from mass spectrometry data-sets. We show (1)
how our method performs on artificial and real-world data-sets, (2) that its
performance is competitive with standard (and widely used) algorithms for
analyzing proteomics data, and (3) that it is robust against random and
systematic noise. We further demonstrate the applicability of our algorithm to
two previously published clinical data-sets
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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