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

arXiv: https://arxiv.org/abs/1801.06607 · HTML: https://mlsanity.com/paper/1801.06607/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2018.

## Built on by (most cited first, 2 of 2)

- [Federated Multilinear Principal Component Analysis with Applications in Prognostics](https://mlsanity.com/paper/2312.06050/) (2023)
- [Hierarchical information matters: Text classification via tree based graph neural network](https://mlsanity.com/paper/2110.02047/) (2021)

## Abstract (arXiv)

A novel text data dimension reduction technique, called the tree-structured multi-linear principal component anal- ysis (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 demon- strated 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.