Adaptive Metric Learning with the Low Rank Constraint

Fang, Yuan; Yan, Yan; Wang, Hanzi; Chen, Si; Gao, Xinbo

doi:10.1145/3007669.3007672

Cited by 1 publication

(3 citation statements)

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“…The methods used for performance comparisons include the Dense Trajectories (DT) method [19], the Low-Rank Metric Learning (LRML) method [12], the Adaptive Metric Learning (ARL) method [13], the Low-Rank Geometric Mean Metric Learning (LR-GMML) method [25], the Multiview Discriminative Analysis of Canonical Correlations (MDACC) method [26], and the multimodal hybrid centroid canonical correlation analysis (MHCCCA) method [27]. The DT, MDACC and MHCCCA methods are the popular and representative video-based action recognition methods.…”

Section: Experiments a Experimental Setupmentioning

confidence: 99%

“…For example, Shen et al [12] obtained a low-dimensional distance metric via the low-rank constraint. Fang et al [13] proposed an adaptive metric learning with the low-rank constraint method. Unfortunately, imposing the commonly-used low-rank constraint (i.e., the trace-norm of the matrix) on the optimization problem of metric learning still leads to high computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…The similarity matrix is learned by taking advantage of Cholesky decomposition. Compared with the traditional low-rank metric learning methods [12], [13], the proposed method achieves the low-rank property of the similarity matrix by limiting the rank of the lower triangular matrix. Therefore, the proposed method is able to simultaneously perform dimensionality reduction and metric learning, where the optimization problem can be easily and efficiently solved via the gradient descent method.…”

Section: Introductionmentioning

confidence: 99%

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Cholesky Decomposition-Based Metric Learning for Video-Based Human Action Recognition

et al. 2020

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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 paper proposes a novel Cholesky decomposition based metric learning (CDML) method for effective video-based human action recognition. Firstly, the improved dense trajectories technique and the vector of locally aggregated descriptor (VLAD) are respectively used for feature detection and feature encoding. Then, considering the high dimensionality of VLAD features, we propose to learn a similarity matrix by taking advantage of Cholesky decomposition, which decomposes the matrix into the product between a lower triangular matrix and its symmetric matrix. Different from the traditional low-rank metric learning methods that explicitly adopt the low-rank constraint to learn the matrix, the proposed algorithm achieves such a constraint by controlling the rank of the lower triangular matrix, thus leading to high computational efficiency. Experimental results on the public video dataset show that the proposed method achieves the superior performance compared with several state-of-the-art methods.

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