Novel dimensionality reduction approach for unsupervised learning on small datasets

作者：

Highlights：

• The combination of F-transform with PCA increases classification accuracy of the original PCA.

• The F-transformation with PCA can solve the unsupervised classification problem with few data.

• The proposal has higher speed and accuracy than Autoencoders.

摘要

•The combination of F-transform with PCA increases classification accuracy of the original PCA.•The F-transformation with PCA can solve the unsupervised classification problem with few data.•The proposal has higher speed and accuracy than Autoencoders.

论文关键词：Unsupervised learning,Dimensionality reduction,PCA,F-transform,Image classification,Autoencoder

论文评审过程：Received 29 November 2018, Revised 1 February 2020, Accepted 19 February 2020, Available online 22 February 2020, Version of Record 2 March 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107291