In many real-world applications, data are represented by matrices or high-order tensors.
Despite the promising performance, the existing 2-D discriminant analysis algorithms …

Robust action recognition under viewpoint changes has received considerable attention
recently. To this end, self-similarity matrices (SSMs) have been found to be effective view …

The number of web images has been explosively growing due to the development of
network and storage technology. These images make up a large amount of current …

Heterogeneous feature representations are widely used in machine learning and pattern
recognition, especially for multimedia analysis. The multi-modal, often also high …

The goal of multilabel classification is to reveal the underlying label correlations to boost the
accuracy of classification tasks. Most of the existing multilabel classifiers attempt to …

We can obtain high-dimensional heterogenous features from real-world images to describe
their various aspects of visual characteristics, such as color, texture and shape etc. Different …

Multi-task learning aims at improving the generalization performance of a learning task with
the help of some other related tasks. Although many multi-task learning methods have been …

Learning explanatory features across multiple related tasks, or MultiTask Feature Selection
(MTFS), is an important problem in the applications of data mining, machine learning, and …

Automatic image annotation (AIA) is very important to image retrieval and image
understanding. Two key issues in AIA are explored in detail in this paper, ie, structured …

Multi-label classification has aroused extensive attention in various fields. With the
emergence of high-dimensional label space, academia has devoted to performing label …