Gene

Linguistics

Engineering

Philosophy

Exploit

Computer security

ENCODE

Set (abstract data type)

Biochemistry

Computer science

Code (set theory)

Correlation

Pattern recognition (psychology)

Mathematics

Politics

Political science

Multi-label classification

Feature (linguistics)

Electronic engineering

Artificial intelligence

Geometry

Sensitivity (control systems)

Representation (politics)

Chemistry

Programming language

Machine learning

It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the label correlations are shared by all the instances. In real-world tasks, however, different instances may share different label correlations, and few correlations are globally applicable. In this paper, we propose the ML-LOC approach which allows label correlations to be exploited locally. To encode the local influence of label correlations, we derive a LOC code to enhance the feature representation of each instance. The global discrimination fitting and local correlation sensitivity are incorporated into a unified framework, and an alternating solution is developed for the optimization. Experimental results on a number of image, text and gene data sets validate the effectiveness of our approach.

Multi-Label Learning by Exploiting Label Correlations Locally