In this paper, we propose a novel ensemble and robust anomaly detection method based on
collaborative representation-based detector. The focused pixels used to estimate the …

Anomaly detection is playing an increasingly important role in hyperspectral image (HSI)
processing. The traditional anomaly detection methods mainly extract knowledge from the …

Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is
important to design an effective prior. For this purpose, we propose a novel image SR …

Low-rank and sparse structures have been profoundly studied in matrix completion and
compressed sensing. In this paper, we develop" Go Decomposition"(GoDec) to efficiently …

Clustering validation has long been recognized as one of the vital issues essential to the
success of clustering applications. In general, clustering validation can be categorized into …

In hyperspectral remote sensing image classification, multiple features, eg, spectral, texture,
and shape features, are employed to represent pixels from different perspectives. It has …

Scene recognition is an important problem in the field of computer vision, because it helps to
narrow the gap between the computer and the human beings on scene understanding …

Nonnegative matrix factorization (NMF) has become a popular data-representation method
and has been widely used in image processing and pattern-recognition problems. This is …

In this paper, a novel recognition algorithm based on discriminant tensor subspace analysis
(DTSA) and extreme learning machine (ELM) is introduced. DTSA treats a gray facial image …

Feature selection is important in many big data applications. Two critical challenges closely
associate with big data. First, in many big data applications, the dimensionality is extremely …