  With the rapid development of social media sharing, people often need to
manage the growing volume of multimedia data such as large scale video
classification and annotation, especially to organize those videos containing
human activities. Recently, manifold regularized semi-supervised learning
(SSL), which explores the intrinsic data probability distribution and then
improves the generalization ability with only a small number of labeled data,
has emerged as a promising paradigm for semiautomatic video classification. In
addition, human action videos often have multi-modal content and different
representations. To tackle the above problems, in this paper we propose
multiview Hessian regularized logistic regression (mHLR) for human action
recognition. Compared with existing work, the advantages of mHLR lie in three
folds: (1) mHLR combines multiple Hessian regularization, each of which
obtained from a particular representation of instance, to leverage the
exploring of local geometry; (2) mHLR naturally handle multi-view instances
with multiple representations; (3) mHLR employs a smooth loss function and then
can be effectively optimized. We carefully conduct extensive experiments on the
unstructured social activity attribute (USAA) dataset and the experimental
results demonstrate the effectiveness of the proposed multiview Hessian
regularized logistic regression for human action recognition.
