Support vector machine (SVM) is an excellent pattern recognition method. Many experiments have shown that SVM can achieve a generalization performance gain by carrying out it in the feature transformation space. Nevertheless, the theoretical foundation behind this phenomenon is presently lack of deep investigation. In the paper, we first give and prove a vital theoretical conclusion that SVM in the feature transformation space can obtain a lower radius-margin bound than one in the feature original space. This means that the performance of SVM can be improved by feature transformation since the radius-margin bound is directly associated with the generalization capacity. Based on this theoretical support, we further propose a novel method called covering-hyperellipsoid-constrained large margin classifier (CHC-LMC). The key characteristic of CHC-LMC is that it jointly learns the minimum bounding hyperellipse and the used classifier by directly minimizing the radius-margin bound in the transformation space, and so embodies the structural risk minimization principle. We develop the linear and nonlinear versions of CHC-LMC and employ an alternate optimization strategy to deal with the corresponding optimization problems. Finally, comprehensive experiments are conducted to verify the validity of CHC-LMC and evaluate the generalization performance by comparing it with the competing methods.

Graph convolutional networks (GCN) have attracted increasing interest in action recognition in recent years. GCN models human skeleton sequences as spatio-temporal graphs. Also, attention mechanisms are often jointly used with GCNs to highlight important frames or body joints in a sequence. However, attention modules learn parameters offline and are fixed, so may not adapt well to unseen samples. In this paper, we propose a simple but effective motion-driven spatial and temporal adaptation strategy to dynamically strengthen the features of important frames and joints for skeleton-based action recognition. The rationale is that the joints and frames with dramatic motions are generally more informative and discriminative. We combine the spatial and temporal refinements by using a two-branch structure, in which the joint and frame-wise feature refinements perform in parallel. Such a structure can lead to learn more complementary feature representations. Moreover, we propose to use the fully connected graph convolution to learn the long-range spatial dependencies. Besides, we investigate two high-resolution skeleton graphs by creating virtual joints, aiming to improve the representation of skeleton features. By combining the above proposals, we develop a novel motion-driven spatial and temporal adaptive high-resolution GCN. Experimental results demonstrate that the proposed model achieves state-of-the-art (SOTA) results on the challenging large-scale Kinetics-Skeleton and UAV-Human datasets, and it is on par with the SOTA methods on the two NTU-RGB+D 60&120 datasets. Additionally, our motion-driven adaptation method shows encouraging performance when compared with the attention mechanisms.

Reducing the redundancy of dominant color features in an image and meanwhile preserving the diversity and quality of extracted colors is of importance in many applications such as image analysis and compression. This paper presents an improved self-organization map (SOM) algorithm namely MFD-SOM and its application to color feature extraction from images. Different from the winner-take-all competitive principle held by conventional SOM algorithms, MFD-SOM prevents, to a certain degree, features of non-principal components in the training data from being weakened or lost in the learning process, which is conductive to preserving the diversity of extracted features. Besides, MFD-SOM adopts a new way to update weight vectors of neurons, which helps to reduce the redundancy in features extracted from the principal components. In addition, we apply a linear neighborhood function in the proposed algorithm aiming to improve its performance on color feature extraction. Experimental results of feature extraction on artificial datasets and benchmark image datasets demonstrate the characteristics of the MFD-SOM algorithm.