A factorization method for the classification of infrared spectra.

ABSTRACT:

Background

Bioinformatics data analysis often deals with additive mixtures of signals for which only class labels are known. Then, the overall goal is to estimate class related signals for data mining purposes. A convenient application is metabolic monitoring of patients using infrared spectroscopy. Within an infrared spectrum each single compound contributes quantitatively to the measurement.

Results

In this work, we propose a novel factorization technique for additive signal factorization that allows learning from classified samples. We define a composed loss function for this task and analytically derive a closed form equation such that training a model reduces to searching for an optimal threshold vector. Our experiments, carried out on synthetic and clinical data, show

SUBMITTER: Henneges C 

PROVIDER: S-EPMC3247165 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature