Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study

TL;DR

This study compares five dimensionality reduction methods for video affect classification, revealing no single best approach and suggesting raw features can sometimes be effective.

Contribution

It provides a comparative analysis of popular dimensionality reduction techniques in affective computing, highlighting their relative performances.

Findings

No approach universally outperforms others.

Using raw features directly can sometimes be effective.

Dimensionality reduction remains a critical challenge in affect classification.

Abstract

Affective computing has become a very important research area in human-machine interaction. However, affects are subjective, subtle, and uncertain. So, it is very difficult to obtain a large number of labeled training samples, compared with the number of possible features we could extract. Thus, dimensionality reduction is critical in affective computing. This paper presents our preliminary study on dimensionality reduction for affect classification. Five popular dimensionality reduction approaches are introduced and compared. Experiments on the DEAP dataset showed that no approach can universally outperform others, and performing classification using the raw features directly may not always be a bad choice.