Nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has
received increasing attention in various practical applications. However, most traditional …

Image registration is an important technique in many computer vision applications such as
image fusion, image retrieval, object tracking, face recognition, change detection and so on …

Correntropy is a local similarity measure defined in kernel space, hence can combat large
outliers in robust signal processing and machine learning. So far, many robust learning …

Although multi-view clustering has received widespread attention due to its far superior
performance to single-view clustering, it still faces the following issues:(1) high …

Correntropy is a local similarity measure defined in kernel space, and the maximum
correntropy criterion (mcc) has been successfully applied in many areas of signal …

The iterative closest point (ICP) algorithm is fast and accurate for rigid point set registration,
but it works badly when handling noisy data or point clouds with outliers. This paper instead …

Constrained adaptive filtering algorithms have been extensively studied in many
applications. Most existing constrained adaptive filtering algorithms are developed under the …

One of the hottest topics in unsupervised learning is how to efficiently and effectively cluster
large amounts of unlabeled data. To address this issue, we propose an orthogonal …

Nonnegative matrix factorization (NMF) has been a vital data representation technique, and
has demonstrated significant potential in the field of machine learning and data mining …

Non-Gaussian noise processing is a difficult and hot spot in the study of filters. A currently
effective method to deal with non-Gaussian noise is replacing the minimum mean square …