In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …

Unlike the visual object tracking, thermal infrared object tracking can track a target object in
total darkness. Therefore, it has broad applications, such as in rescue and video …

Abstract Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for
feature extraction in recent years. By decomposing the matrix recurrently on account of the …

Multiview data processing has attracted sustained attention as it can provide more
information for clustering. To integrate this information, one often utilizes the non-negative …

Non-negative matrix factorization (NMF), a method for finding parts-based representation of
non-negative data, has shown remarkable competitiveness in data analysis. Given that real …

Most thermal infrared (TIR) tracking methods are discriminative, treating the tracking
problem as a classification task. However, the objective of the classifier (label prediction) is …

Current unsupervised feature selection methods cannot well select the effective features
from the corrupted data. To this end, we propose a robust unsupervised feature selection …

Manifold learning aims to discover the low dimensional space where the input high
dimensional data are embedded by preserving the geometric structure. Unfortunately …

Non-negative matrix factorization (NMF) has attracted much attention for multi-view
clustering due to its good theoretical and practical values. Although existing multi-view NMF …

Real-world data sets are often comprised of multiple representations or modalities which
provide different and complementary aspects of information. Multi-view clustering plays an …