Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study
This is an incremental study for researchers in affective computing, addressing the challenge of high-dimensional features with limited labeled data.
The paper tackled the problem of feature dimensionality reduction for video affect classification by comparing five popular approaches on the DEAP dataset, finding that no method universally outperformed others and that using raw features directly can sometimes be effective.
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.