Regression-Based Feature Selection on Large Scale Human Activity Recognition
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 2
Abstract
In this paper, we present an approach for regression-based feature selection in human activity recognition. Due to high dimensional features in human activity recognition, the model may have over-fitting and can’t learn parameters well. Moreover, the features are redundant or irrelevant. The goal is to select important discriminating features to recognize the human activities in videos. R-Squared regression criterion can identify the best features based on the ability of a feature to explain the variations in the target class. The features are significantly reduced, nearly by 99.33%, resulting in better classification accuracy. Support Vector Machine with a linear kernel is used to classify the activities. The experiments are tested on UCF50 dataset. The results show that the proposed model significantly outperforms state-of-the-art methods.
Authors and Affiliations
Hussein Mazaar, Eid Emary, Hoda Onsi
Keywords
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- EP ID EP117724
- DOI 10.14569/IJACSA.2016.070283
- Views 109
- Downloads 0
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