  The ever-growing popularity of Kinect and inertial sensors has prompted
intensive research efforts on human action recognition. Since human actions can
be characterized by multiple feature representations extracted from Kinect and
inertial sensors, multiview features must be encoded into a unified space
optimal for human action recognition. In this paper, we propose a new
unsupervised feature fusion method termed Multiview Cauchy Estimator Feature
Embedding (MCEFE) for human action recognition. By minimizing empirical risk,
MCEFE integrates the encoded complementary information in multiple views to
find the unified data representation and the projection matrices. To enhance
robustness to outliers, the Cauchy estimator is imposed on the reconstruction
error. Furthermore, ensemble manifold regularization is enforced on the
projection matrices to encode the correlations between different views and
avoid overfitting. Experiments are conducted on the new Chinese Academy of
Sciences - Yunnan University - Multimodal Human Action Database (CAS-YNU-MHAD)
to demonstrate the effectiveness and robustness of MCEFE for human action
recognition.
