Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis
TABLE V: Error Rate (%)
['[EMPTY]', 'TMPCA+SVM', 'PCA+SVM', 'SVM', 'RNN']
This substantiates the claim that TMPCA is better in preserving the structure of input sentences. In addition, the fact that SVM performs better on reduced datasets demonstrates that the TMPCA is able to remove the weakly or non-correlated information from the dataset while preserving the principal ones. This helps alleviate the overfitting problem.