A Novel Angle-Based Learning Framework on Semi-supervised Dimensionality Reduction in High-Dimensional Data with Application to Action Recognition

The existing outliers in high-dimensional data create various challenges to classify datasets such as the exact classification with imbalanced scatters. In this paper, we propose an angle-based framework as Angle Global and Local Discriminant Analysis (AGLDA) to consider imbalanced scatters. AGLDA chooses an optimal subspace by using angle cosine to achieve appropriate scatter balance in the dataset. The privilege of this method is to classify datasets with the effect of outliers by finding optimal subspace in high-dimensional data. Generally, this method is more effective and more reliable than other methods to classify data when there are outliers. Besides, human posture classification has been used as an application of the balanced semi-supervised dimensionality reduction to assist human factor experts and designers of industrial systems for diagnosing the type of maintenance crew postures. The experimental results show the efficiency of the proposed method via two real case studies, and the results have also been verified by comparing it with other approaches.