Supervised dimensionality reduction of proportional data using mixture estimation

作者：

Highlights：

• The dimensionality of data is reduced effectively such that different classes of data are easily separable.

• Sparsity of data does not affect the efficiency and effectiveness of the algorithm.

• The algorithm remains efficient even for significantly large number of features.

摘要

•The dimensionality of data is reduced effectively such that different classes of data are easily separable.•Sparsity of data does not affect the efficiency and effectiveness of the algorithm.•The algorithm remains efficient even for significantly large number of features.

论文关键词：Dimensionality reduction,Feature extraction

论文评审过程：Received 7 January 2019, Revised 15 March 2020, Accepted 14 April 2020, Available online 29 April 2020, Version of Record 11 May 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107379