Learning shared subspace regularization with linear discriminant analysis for multilabel action recognition Human action recognition under complex environment is a challenging work, while in deep learning and in these specific difficulty recognition tasks, the multilabel 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 highdimensional 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.

Subspace Learning for Silhouette Based Human Action Recognition This paper exploits different subspace learning methods applied on silhouette based action recognition and evaluates their performance. Our recognition scheme is formed by segmenting action sequence into overlapped subclips and using submodels for action matching. This submodel matching method shows advantages in processing periodic actions. The experimental results prove that human action silhouettes are very informative for action recognition and subspace analysis can effectively preserve the intrinsic structure of raw data from 3D silhouettes. The subspace learning methods compared in this paper include traditional methods  Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and recently reported Orthogonal Local Preserving Projection (OLPP). PCA is observed to perform the best regarding both accuracy and efficiency. We believe our work is helpful for further research in silhouette based action recognition combined with subspace learning methods.