Comparison of LLE and PCA Algorithms for Gene Expression Data Analysis

Abstract:

According to the problem that the linear dimension reduction is not effective to understand gene expression data. using the manifold learning as a guide, analysing dimensionality reduction of gene expression data, selecting colon cancer and leukaemia gene expression datasets for investigation, using inter category distances as the criteria to quantitatively evaluate the effects of data dimensionality reduction. Experiments show that LLE algorithm is more suitable method for the gene expression data. The LLE analyses indicate that there is a clear distinction boundary between the healthy people and the cancer patients.

• Periodical: Applied Mechanics and Materials (Volumes 513-517)
• Pages: 378-381
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.378
• Online since: February 2014
• Authors: Xiao Zhou Chen*, Fan Yang, Hua Mei Li, Jun Hua Chen
• Keywords: Dimensional Reduction, Gene Expression Data, LLE Algorithm, PCA Algorithm

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