The issue of electroencephalogram (EEG)-based emotion recognition has great academic
and practical significance. Currently, there are numerous research trying to address this …

Most popular clustering methods map raw image data into a projection space in which the
clustering assignment is obtained with the vanilla k-means approach. In this article, we …

In this survey, we propose to explore and discuss the common rules behind knowledge
transfer works for vision recognition tasks. To achieve this, we firstly discuss the different …

Artificial intelligent systems are changing every aspect of our daily life. In the past decades,
numerous approaches have been developed to characterize user behavior, in order to …

Transfer learning has attracted great attention to facilitate the sparsely labeled or unlabeled
target learning by leveraging previously well-established source domain through knowledge …

Unsupervised domain adaptation (UDA) has attracted increasing attention in recent years,
which adapts classifiers to an unlabeled target domain by exploiting a labeled source …

We develop a fine-grained image classifier using a general deep convolutional neural
network (DCNN). We improve the fine-grained image classification accuracy of a DCNN …

Ensemble Clustering (EC) is an important topic for data cluster analysis. It targets to
integrate multiple Basic Partitions (BPs) of a particular dataset into a consensus partition …

Recently, there is a rapidly increasing attraction for the efficient recovery of low-rank matrix
in computer vision and machine learning. The popular convex solution of rank minimization …

This paper deals with sparse signal reconstruction by designing a discrete-time projection
neural network. Sparse signal reconstruction can be converted into an-minimization …