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Cholesky Decomposition Based Metric Learning for Video-based Human Action Recognition
2020
IEEE Access
Video-based human action recognition can understand human actions and behaviours in the video sequences, and has wide applications for health care, human-machine interaction and so on. Metric learning, which learns a similarity metric, plays an important role in human action recognition. However, learning a full-rank matrix is usually inefficient and easily leads to overfitting. In order to overcome the above issues, a common way is to impose the low-rank constraint on the learned matrix. This
doi:10.1109/access.2020.2966329
fatcat:vkekokypynghhfohktkeihs5y4