Locality and label information of training samples play an important role in image
classification. However, previous dictionary learning algorithms do not take the locality and …

Both interclass variances and intraclass similarities are crucial for improving the
classification performance of discriminative dictionary learning (DDL) algorithms. However …

This book revolves around the question of designing a matrix D∈ Rm× n called dictionary,
such that to obtain good sparse representations y≈ Dx for a class of signals y∈ Rm given …

As a major concern, the existence of unwanted energy and missing traces in seismic data
acquisition can degrade interpretation of such data after processing. Instead of analytical …

This study presents a comprehensive review of the automated classification in partial
discharge (PD) source identification and probabilistic interpretation of the classification …

Neural activities recorded using electroencephalography (EEG) are mostly contaminated
with eye blink (EB) artifact. This results in undesired activation of brain-computer interface …

In histopathological image analysis, feature extraction for classification is a challenging task
due to the diversity of histology features suitable for each problem as well as presence of …

In this paper, a robust sequential dictionary learning (DL) algorithm is presented. The
proposed algorithm is motivated from the maximum likelihood perspective on dictionary …

In transfer learning model, the source domain samples and target domain samples usually
share the same class labels but have different distributions. In general, the existing transfer …

Blind hyperspectral unmixing involves jointly estimating endmembers and fractional
abundances in hyperspectral images. An endmember is the spectral signature of a specific …