Next Article in Journal
Diffusion Maximum Correntropy Criterion Based Robust Spectrum Sensing in Non-Gaussian Noise Environments
Next Article in Special Issue
Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm
Previous Article in Journal
Entropy-Based Video Steganalysis of Motion Vectors
Previous Article in Special Issue
Deconstructing Cross-Entropy for Probabilistic Binary Classifiers
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Entropy 2018, 20(4), 245; https://doi.org/10.3390/e20040245

Multi-Graph Multi-Label Learning Based on Entropy

College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Received: 25 March 2018 / Revised: 30 March 2018 / Accepted: 30 March 2018 / Published: 2 April 2018
(This article belongs to the Special Issue Entropy-based Data Mining)
View Full-Text   |   Download PDF [2997 KB, uploaded 3 May 2018]   |  

Abstract

Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of graphs and each bag is marked with multiple class labels. It is an interesting problem existing in many applications, such as image classification, medicinal analysis and so on. In this paper, we propose an innovate algorithm to address the problem. Firstly, it uses more precise structures, multiple Graphs, instead of Instances to represent an image so that the classification accuracy could be improved. Then, it uses multiple labels as the output to eliminate the semantic ambiguity of the image. Furthermore, it calculates the entropy to mine the informative subgraphs instead of just mining the frequent subgraphs, which enables selecting the more accurate features for the classification. Lastly, since the current algorithms cannot directly deal with graph-structures, we degenerate the Multi-Graph Multi-Label Learning into the Multi-Instance Multi-Label Learning in order to solve it by MIML-ELM (Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine). The performance study shows that our algorithm outperforms the competitors in terms of both effectiveness and efficiency. View Full-Text
Keywords: multi-graph multi-label; entropy; informative subgraphs; extreme learning machine multi-graph multi-label; entropy; informative subgraphs; extreme learning machine
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhu, Z.; Zhao, Y. Multi-Graph Multi-Label Learning Based on Entropy. Entropy 2018, 20, 245.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top