Locality Constrained Dictionary Learning Research Articles

Locality constrained dictionary learning for human behaviour recognition

ABSTRACTImprovement of the accuracy and robustness of human behaviour recognition is still a challenging problem. In this paper, a human behaviour recognition method based on locality constrained dictionary learning (LCDL) is proposed, in which two global features, namely average motion energy image (AMEI) and enhanced motion energy image (EMEI), are employed for the description of human behaviours. A discriminative structured dictionary is learned by the LCDL algorithm, whose sub-dictionary corresponds to each type of human behaviour. Moreover, the sparse representation errors of the testing samples are used for the recognition. The results of simulations and performance comparisons on typical datasets show that the proposed methods can reduce the storage space and calculation quantity through the normalization treatment of AMEIs and EMEIs. Since the locality constrained conditions can enforce the intra-class distance and improve the discriminative ability of the structured dictionary, the recognition performance is improved.

Locality constrained dictionary learning for non‐linear dimensionality reduction and classification

In view of the incremental dimensionality reduction problem of existing non-linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study. During the dictionary learning process, the neighbourhood size of some potential landmarks on a non-linear manifold is constrained to maintain the intrinsic local geometric feature of the datasets. Meanwhile, to improve the dictionary's discrimination ability, a structured dictionary is learnt by LCDL, whose sub-dictionaries are class-specific. Then sparse coding and its reconstruction errors are used for classification. The experimental results of dimensionality reduction prove that, compared with the existing methods, the proposed method can solve the out of sample extension and large-scale datasets problems efficiently. In addition, the experimental results of face, gender, and object category classification demonstrate that the authors' algorithm outperforms some competing dictionary learning methods.

Locality Constrained Dictionary Learning for Nonlinear Dimensionality Reduction

Current nonlinear dimensionality reduction (NLDR) algorithms have quadratic or cubic complexity in the number of data, which limits their ability to process real-world large-scale datasets. Learning over a small set of landmark points can potentially allow much more effective NLDR and make such algorithms scalable to large dataset problems. In this paper, we show that the approximation to an unobservable intrinsic manifold by a few latent points residing on the manifold can be cast in a novel dictionary learning problem over the observation space. This leads to the presented locality constrained dictionary learning (LCDL) algorithm, which effectively learns a compact set of atoms consisting of locality-preserving landmark points on a nonlinear manifold. Experiments comparing state-of-the-art DL algorithms, including K-SVD, LCC and LLC, show that LCDL improves the embedding quality and greatly reduces the complexity of NLDR algorithms.