Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …

With the popularity of cameras and sensors, massive data are captured from various view
angles or modalities, which provide abundant complementary information and also bring …

Human activity recognition using videos sequences is a well-known phenomenon which has
many real-life applications such as daily assistive living, security and surveillance, patient …

Maximum margin clustering (MMC) is a typical clustering method which aims to maximize
the margin between different clusters. However, in practical applications, a data object may …

Extracting features from EEG signals through time, frequency and spatial-domain gives rise
to the problem of neglecting the property of nonlinear manifold structure of the data, and …

Unsupervised clustering for high-dimension data (such as imageset or video) is a hard issue
in data processing and data mining area since these data always lie on a manifold (such as …

This paper proposes a novel algorithm for computing discriminative descriptors named as a
sparse coded composite descriptor (SCCD) for robust human activity recognition. The …

Domain adaptation in the Euclidean space is a challenging task on which researchers
recently have made great progress. However, in practice, there are rich data representations …

The low-rank matrix optimization with affine manifold (rank-MOA) aims to minimize a
continuously differentiable function over a low-rank set intersecting with an affine manifold …

Ballet and sports dance are also dance art from the west, which are popular among the
Chinese people after they were introduced into China. With the popularization of compulsory …