Kernel based nonlinear dimensionality reduction for microarray gene expression data analysis

Authors:
Xuehua Li;Lan Shu
Affiliations:
School of Applied Mathematics, University of Electronic Science and Technology of China, Jianshe Road, Chengdu 610054, PR China;School of Applied Mathematics, University of Electronic Science and Technology of China, Jianshe Road, Chengdu 610054, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 8
Cited 4

The nature of statistical learning theory

The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

SIAM Journal on Scientific Computing
Face Recognition with Weighted Locally Linear Embedding

CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Feature selection for the SVM: An application to hypertension diagnosis

Expert Systems with Applications: An International Journal
Nonlinear dimensionality reduction of data manifolds with essential loops

Neurocomputing

Robust approach for estimating probabilities in Naïve-Bayes Classifier for gene expression data

Expert Systems with Applications: An International Journal
An efficient statistical feature selection approach for classification of gene expression data

Journal of Biomedical Informatics
Gene expression data classification based on improved semi-supervised local Fisher discriminant analysis

Expert Systems with Applications: An International Journal
Feature Extraction Using a Complete Kernel Extension of Supervised Graph Embedding

Neural Processing Letters

Quantified Score

Hi-index	12.05

Visualization

Abstract

Accurate recognition of cancers based on microarray gene expressions is very important for doctors to choose a proper treatment. Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. This paper proposed the kernel method based locally linear embedding to selecting the optimal number of nearest neighbors, constructing uniform distribution manifold. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding is proposed to select the optimal number of nearest neighbors, constructing uniform distribution manifold. In addition, support vector machine which has given rise to the development of a new class of theoretically elegant learning machines will be used to classify and recognise genomic microarray. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results and comparisons demonstrate that the proposed method is effective approach.