Volume 2, Issue 4, December 2017, Page: 125-132
Unsupervised Dimensionality Reduction for High-Dimensional Data Classification
Hany Yan, School of Mathematics, Jilin University, Changchun, China
Hu Tianyu, School of Mathematics, Jilin University, Changchun, China
Received: Jul. 20, 2017;       Accepted: Aug. 9, 2017;       Published: Aug. 31, 2017
DOI: 10.11648/j.mlr.20170204.13      View  2170      Downloads  221
This paper carries on research surrounding the influences produced by dimensionality reduction on machine learning classification effect. Firstly, paper constructs the analysis architecture of data dimension reduction classification, combines the two different unsupervised dimension reduction methods, locally linear embedding (LLE) and principal component analysis (PCA) with the five machine learning classification methods: Gradient Boosting Decision Tree (GBDT), Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Logistic Regression. And then uses the handwritten digital identification dataset to analyze the classification performance of these five classification methods on different dimension datasets by different dimensionality reduction methods. The analysis shows that using the appropriate dimensionality reduction method for dimensionality reduction classification can effectively improve the classification accuracy; the dimensionality reduction classification effect of non-linear dimensionality reduction method is generally better than the linear dimensionality reduction method; different machine learning classification algorithms have significant differences in the sensitivity of dimensions.
Dimensionality Reduction, Machine Learning, Classification Problem, Handwritten Numeral Recognition
To cite this article
Hany Yan, Hu Tianyu, Unsupervised Dimensionality Reduction for High-Dimensional Data Classification, Machine Learning Research. Vol. 2, No. 4, 2017, pp. 125-132. doi: 10.11648/j.mlr.20170204.13
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