Of late, there are many studies on the robust discriminant analysis, which adopt L 1-norm as
the distance metric, but their results are not robust enough to gain universal acceptance. To …

Among various physiological signal acquisition methods for the study of the human brain,
EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive …

Class imbalance is a common issue in the community of machine learning and data mining.
The class-imbalance distribution can make most classical classification algorithms neglect …

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

We present a new deep dictionary learning and coding network (DDLCN) for image-
recognition tasks with limited data. The proposed DDLCN has most of the standard deep …

In recent years, with the rapid growth of rooftop photovoltaic (PV) generation in distribution
networks, power system operators call for accurate forecasts of the behind-the-meter (BTM) …

Industrial process data have the characteristics of less label, multimode, high dimension,
containing noise, and mixing with outliers, which increase the difficulty of mode identification …

In this article, we propose a structured robust adaptive dictionary pair learning (RA-DPL)
framework for the discriminative sparse representation (SR) learning. To achieve powerful …

With the development of smart cities, urban surveillance video analysis plays a further
significant role in intelligent transportation systems. Vehicle re-identification (re-ID) aims at …

Anomaly detection based on telemetry data is a major issue in satellite health monitoring,
given that it can identify unusual or unexpected events to avoid serious accidents and …