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We present a novel transfer learning approach to cross-camera action recognition. Inspired by canonical correlation analysis (CCA), we first extract the spatio-temporal visual words from videos captured at different views, and derive a correlation subspace as a joint representation for different bag-of-words models at different views. Different from prior CCA-based approaches which simply train standard classifiers such as SVM in the resulting subspace, we explore the domain transfer ability of CCA in the correlation subspace, in which each dimension has a different capability in correlating source and target data. In our work, we propose a novel SVM with a correlation regularizer which incorporates such ability into the design of the SVM. Experiments on the IXMAS dataset verify the effectiveness of our method, which is shown to outperform state-of-the-art transfer learning approaches without taking such domain transfer ability into consideration.

We aim at providing a comprehensive introduction to Support Vector Machines and their applications in computational finance. Based on the advances of the statistical learning theory, one of the first SVM algorithms was proposed in mid 1990s. Since then, they have drawn a lot of research interests both in theoretical and application domains and have became the state-of-the-art techniques in solving classification and regression problems. The reason for the success is not only because of their sound theoretical foundation but also their good generalization performance in many real applications. In this chapter, we address the theoretical, algorithmic and computational issues and try our best to make the article self-contained. Moreover, in the end of this chapter, a case study on default prediction is also presented. We discuss the issues when SVM algorithms are applied to bankruptcy prognosis such as how to deal with the unbalanced dataset, how to tune the parameters to have a better performance and how to deal with large scale dataset.

Outlier detection is an important issue in datamining and has been studied in different research areas. It can be used for detecting the small amount of deviated data. In this article, we use “Leave One Out” procedure to check each individual point the “with or without” effect on the variation of principal directions. Based on this idea, an over-sampling principal component analysis outlier detection method is proposed for emphasizing the influence of an abnormal instance (or an outlier). Except for identifying the suspicious outliers, we also design an on-line anomaly detection to detect the new arriving anomaly. In addition, we also study the quick updating of the principal directions for the effective computation and satisfying the on-line detecting demand. Numerical experiments show that our proposed method is effective in computation time and anomaly detection.