Abstract

A novel text data dimension reduction technique, called the tree-structured multi-linear principal component analysis (TMPCA), is proposed in this work. Being different from traditional text dimension reduction methods that deal with the word-level representation, the TMPCA technique reduces the dimension of input sequences and sentences to simplify the following text classification tasks. It is shown mathematically and experimentally that the TMPCA tool demands much lower complexity (and, hence, less computing power) than the ordinary principal component analysis (PCA). Furthermore, it is demonstrated by experimental results that the support vector machine (SVM) method applied to the TMPCA-processed data achieves commensurable or better performance than the state-of-the-art recurrent neural network (RNN) approach.

Date

2018

Authors

Yuanhang Su, Yuzhong Huang, C-C Jay Kuo

Conference

2018 24th international conference on pattern recognition (ICPR)

Pages

585-590

Publisher

IEEE