Learning dictionary for sparse representing time series is an important issue to extract latent
temporal features, reveal salient primitives and sparsely represent complex temporal data …

In this article, we propose a novel multi-task hybrid dictionary learning approach for moving
vehicle classification tasks using multi-sensor networks to improve the classification …

Gathering labeled data is one of the most time-consuming and expensive tasks in
supervised machine learning. In practical applications, there are usually quite limited …

Given the importance of fMRI (including both tfMRI and rsfMRI) data for functional brain
mapping, tremendous efforts have been devoted on the establishment of fMRI informatics …

Classification methods based on sparse estimation have drawn much attention recently, due
to their effectiveness in processing high-dimensional data such as images. In this paper, a …

Multiple visual features are represented by multimedia data. The focus is on the semi-
supervised learning when the label information of the training data is insufficient. Most of the …

Rolling bearing plays an important role in industrial applications, and the analysis of
vibration is widely used in the bearing fault diagnosis. This paper presents a novel model for …

The classification of gene expression profiles has become an important means of cancer
classification. As a new machine learning method, dictionary learning has become more and …

The assumption that training samples and test samples obey the same distribution is grossly
violated when images are from distinct domains, which can lead to degradation in …

Dictionary learning is a popular method for obtaining sparse linear representations for high
dimensional data, with many applications in image classification, signal processing and …