Learning shared subspace regularization with linear discriminant analysis for multi-label action recognition

摘要

Human action recognition under complex environment is a challenging work, while in deep learning and in these specific difficulty recognition tasks, the multi-label linear discriminant analysis (MLDA) is already utilized. As is known to all, MLDA is used for dimensionality feature reduction. Nevertheless, MLDA contains the eigendecomposition of dense matrices, which will cost a huge money on a high dimension computing. In this study, we demonstrate that the MLDA formula is able to equivalent to the least squares problem, which greatly reduces the computation and size of high-dimensional datasets. In addition, it is found that introducing attractive regularization technique into the classical least squares strategy can improve the robustness. The established equivalence relationship is proved by laboratory results on three action datasets. In addition, through comparing with some typical and recent algorithms, the superiority of our constructed model is also demonstrated.