Data-driven dictionaries have produced state-of-the-art results in various classification tasks.
However, when the target data has a different distribution than the source data, the learned …

Sparse coding represents a signal sparsely by using an overcomplete dictionary, and
obtains promising performance in practical computer vision applications, especially for …

Sparse representation based classification (SRC) has recently been proposed for robust
face recognition. To deal with occlusion, SRC introduces an identity matrix as an occlusion …

Recognition of natural emotions from human faces is an interesting topic with a wide range
of potential applications, such as human-computer interaction, automated tutoring systems …

The success of sparse representation based classification (SRC) has largely boosted the
research of sparsity based face recognition in recent years. A prevailing view is that the …

We propose requirements monitoring to aid in the maintenance of systems that reside in
dynamic environments. By requirements monitoring we mean the insertion of code into a …

Sparse representation provides an effective tool for classification under the conditions that
every class has sufficient representative training samples and the training data are …

Traditional sparse image models treat color image pixel as a scalar, which represents color
channels separately or concatenate color channels as a monochrome image. In this paper …

This paper proposes a new deep learning method, the greedy deep weighted dictionary
learning for mobile multimedia for medical diseases analysis. Based on the traditional …

Mechanical equipment such as wind turbines often operates under time-varying working
conditions (TVWC). The vibration signals collected from their key rotating components, such …