One paper is online on Neurocomputing. Con~ to Hongli!

Hessian Regularization by Patch Alignment Framework

In recent years, semi-supervised learning has played a key part in large-scale image management, where usually only a few images are labeled. To address this problem, many representative works have been reported, including transductive SVM, universum SVM, co-training and graph-based methods. The prominent method is the patch alignment framework, which unifies the traditional spectral analysis methods. In this paper, we propose Hessian regression based on the patch alignment framework. In particular, we construct a Hessian using the patch alignment framework and apply it to regression problems. To the best of our knowledge, there is no report on Hessian construction from the patch alignment viewpoint. Compared with the traditional Laplacian regularization, Hessian can better match the data and then leverage the performance. To validate the effectiveness of the proposed method, we conduct human face recognition experiments on a celebrity face dataset. The experimental results demonstrate the superiority of the proposed solution in human face classification.

Semi-supervised learning; Hessian; Patch alignment; Least Squares



TIE paper entitled “p-Laplacian Regularized Sparse Coding for Human Activity Recognition” is online.

p-Laplacian Regularized Sparse Coding for Human Activity Recognition

W. Liu 
Weifeng Liu is with the College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China (email: liuwf@ upc.edu.cn). 
Z. J. Zha ; Y. Wang ; K. Lu ; D. Tao

Human activity analysis in videos has increasingly attracted attention in computer vision research with the massive number of videos now accessible online. Although many recognition algorithms have been reported recently, activity representation is challenging. Recently, manifold regularized sparse coding has obtained promising performance in action recognition, because it simultaneously learns the sparse representation and preserves the manifold structure. In this paper, we propose a generalized version of Laplacian regularized sparse coding for human activity recognition called p-Laplacian regularized sparse coding. The proposed method exploits p-Laplacian regularization to preserve the local geometry. The p-Laplacian is a nonlinear generalization of standard graph Laplacian and has tighter isoperimetric inequality. As a result, p-Laplacian regularized sparse coding provides superior theoretical evidence than standard Laplacian regularized sparse coding with a proper p. We also provide a fast iterative Shrinkage-Thresholding algorithm (FISTA) for the optimization of p-Laplacian regularized sparse coding. Lastly, we input the sparse codes learned by the p- Laplacian regularized sparse coding algorithm into support vector machines and conduct extensive experiments on the unstructured social activity attribute (USAA) dataset and human motion database (HMDB51) for human activity recognition. The experimental results demonstrate that the proposed p-Laplacian regularized sparse coding algorithm outperforms the manifold regularized sparse coding algorithms including the standard Laplacian regularized sparse coding algorithm with a proper p.

human activity recognition
sparse coding