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A novel text data dimension reduction technique, called the tree-structuredmulti-linear principal component anal- ysis (TMPCA), is proposed in this work.Being different from traditional text dimension reduction methods that dealwith the word-level representation, the TMPCA technique reduces the dimensionof input sequences and sentences to simplify the following text classificationtasks. It is shown mathematically and experimentally that the TMPCA tooldemands much lower complexity (and, hence, less computing power) than theordinary principal component analysis (PCA). Furthermore, it is demon- stratedby experimental results that the support vector machine (SVM) method applied tothe TMPCA-processed data achieves commensurable or better performance than thestate-of-the-art recurrent neural network (RNN) approach.

Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis