I have a dataset of dimension 3,000 x 24,000 (approximately) with 6 class label. But the data is very sparse. The number of non-zero values per sample ranges from 10-300 (approx) out of 24,000. The non-zero values in the dataset are real numbers. I need to perform feature selection/reduction before the classification. Which technique would be better for such dataset?
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$\begingroup$ Have you considered L1 regularization? $\endgroup$ – spdrnl Nov 29 '17 at 15:45
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$\begingroup$ I am trying to apply elasticnet for feature selection. $\endgroup$ – Md. Abid Hasan Nov 29 '17 at 19:22
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$\begingroup$ Elasticnet is L1 and L2; for feature selection L1 only offers better functionality AFAIK. $\endgroup$ – spdrnl Nov 30 '17 at 15:34
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Feature selection and feature reduction are two very different strategies.
Generally speaking, non-parametric tests is probably your best option, I'd go towards a Kruskal-Wallis rank sum test to get overall differential features or a Mann-Whitney rank sum test for each class label.
For feature reduction, I believe zero-inflated factorial analysis (ZIFA) is a good solution (Pierson, Emma, and Christopher Yau. "ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis." Genome biology 16.1 (2015): 241.). However, more classical factor analysis, NMF or t-SNE may work.
Now the data you describe really looks a lot like a single-cell RNAseq dataset. If this is the case, I encourage you to take a look at the following resource: https://hemberg-lab.github.io/scRNA.seq.course/biological-analysis.html#de-in-a-real-dataset
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$\begingroup$ I believe it is more correct to say factor analysis than factorial analysis, despite I don't know because didn't read those articles you cite. We say, factorial design, but that is very different. $\endgroup$ – ttnphns Nov 29 '17 at 11:44
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$\begingroup$ Edit. Thanks for the comment, it was an incorrect translation. $\endgroup$ – Rémy Nicolle Nov 29 '17 at 13:05
