An Efficient Dimensionality Reduction Approach for Small-sample Size and High-dimensional Data Modeling

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Provided by: Academy Publisher
Topic: Big Data
Format: PDF
As for massive multidimensional data are being generated in a wide range of emerging applications, this paper introduces two new methods of dimension reduction to conduct small-sample size and high-dimensional data processing and modeling. Through combining the Support Vector Machine (SVM) and Recursive Feature Elimination (RFE), SVM-RFE algorithm is proposed to select features, and further, adding the Higher Order Singular Value Decomposition (HOSVD) to the feature extraction which involves successfully organizing the data into high order tensor pattern.
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