Diversified dictionaries for multi-instance learning

Journal article


Qiao, Maoying, Liu, Liu, Yu, Jun, Xu, Chang and Tao, Dacheng. (2017) Diversified dictionaries for multi-instance learning. Pattern Recognition. 64, pp. 407-416. https://doi.org/10.1016/j.patcog.2016.08.026
AuthorsQiao, Maoying, Liu, Liu, Yu, Jun, Xu, Chang and Tao, Dacheng
Abstract

Multiple-instance learning (MIL) has been a popular topic in the study of pattern recognition for years due to its usefulness for such tasks as drug activity prediction and image/text classification. In a typical MIL setting, a bag contains a bag-level label and more than one instance/pattern. How to bridge instance-level representations to bag-level labels is a key step to achieve satisfactory classification accuracy results. In this paper, we present a supervised learning method, diversified dictionaries MIL, to address this problem. Our approach, on the one hand, exploits bag-level label information for training class-specific dictionaries. On the other hand, it introduces a diversity regularizer into the class-specific dictionaries to avoid ambiguity between them. To the best of our knowledge, this is the first time that the diversity prior is introduced to solve the MIL problems. Experiments conducted on several benchmark (drug activity and image/text annotation) datasets show that the proposed method compares favorably to state-of-the-art methods.

Keywordsmulti-instance learning; diversified learning; dictionary learning
Year2017
JournalPattern Recognition
Journal citation64, pp. 407-416
PublisherElsevier Ltd
ISSN0031-3203
Digital Object Identifier (DOI)https://doi.org/10.1016/j.patcog.2016.08.026
Scopus EID2-s2.0-85007162586
Research or scholarlyResearch
Page range407-416
FunderAustralian Research Council
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online26 Aug 2016
Publication process dates
Accepted22 Aug 2016
Deposited10 Jun 2021
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDARC/FT130101457
ARC/DP140102164
ARC/LE140100061
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