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Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data
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DOI：10.4103/jmss.jmss_52_25.png)
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Abstract
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Background:Sparse representation (SR) has shown strong performance in classification tasks, particularly for high-dimensional data such as microarray gene expression profiles. These datasets present significant challenges due to their high dimensionality and limited sample size, which often hinder the performance of conventional classifiers.Methods:SR addresses this by expressing each signal as a linear combination of a small subset of training samples, reducing computational complexity and improving accuracy. However, using all training samples in the dictionary increases computational cost. This study explores several SR-based classifiers to address microarray data classification, focusing on dictionary construction strategies and sparse coding algorithms.Results:Experimental results on the 14-Tumors dataset show that selecting a subset of representative atoms and applying the SL0 algorithm significantly improves both speed and classification accuracy.Conclusions:These findings highlight the potential of SR approaches for effective and efficient classification of high-dimensional biological data.
Keywords:
Computational biology
dictionary learning
gene expression
hierarchical classification
high-dimensional data
microarray data classification
sparse representation
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Papers:
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