Regression-Based Feature Selection on Large Scale Human Activity Recognition

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.

@article{Mazaar2016,
  title     = {Regression-Based Feature Selection on Large Scale Human Activity Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {2},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Hussein Mazaar and Eid Emary and Hoda Onsi},
  doi       = {10.14569/IJACSA.2016.070283},
  url       = {https://doi.org/10.14569/IJACSA.2016.070283}
}