Multiple instance learning with manifold bags
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Multiple instance learning with manifold bags. / Babenko, Boris; Verma, Nakul; Dollár, Piotr; Belongie, Serge.
In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, 2011, p. 81-88.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Multiple instance learning with manifold bags
AU - Babenko, Boris
AU - Verma, Nakul
AU - Dollár, Piotr
AU - Belongie, Serge
PY - 2011
Y1 - 2011
N2 - In many machine learning applications, labeling every instance of data is burdensome. Multiple Instance Learning (MIL), in which training data is provided in the form of labeled bags rather than labeled instances, is one approach for a more relaxed form of supervised learning. Though much progress has been made in analyzing MIL problems, existing work considers bags that have a finite number of instances. In this paper we argue that in many applications of MIL (e.g. image, audio, etc.) the bags are better modeled as low dimensional manifolds in high dimensional feature space. We show that the geometric structure of such manifold bags affects PAC-learnability. We discuss how a learning algorithm that is designed for finite sized bags can be adapted to learn from manifold bags. Furthermore, we propose a simple heuristic that reduces the memory requirements of such algorithms. Our experiments on real-world data validate our analysis and show that our approach works well.
AB - In many machine learning applications, labeling every instance of data is burdensome. Multiple Instance Learning (MIL), in which training data is provided in the form of labeled bags rather than labeled instances, is one approach for a more relaxed form of supervised learning. Though much progress has been made in analyzing MIL problems, existing work considers bags that have a finite number of instances. In this paper we argue that in many applications of MIL (e.g. image, audio, etc.) the bags are better modeled as low dimensional manifolds in high dimensional feature space. We show that the geometric structure of such manifold bags affects PAC-learnability. We discuss how a learning algorithm that is designed for finite sized bags can be adapted to learn from manifold bags. Furthermore, we propose a simple heuristic that reduces the memory requirements of such algorithms. Our experiments on real-world data validate our analysis and show that our approach works well.
UR - http://www.scopus.com/inward/record.url?scp=80053449506&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:80053449506
SP - 81
EP - 88
JO - Proceedings of the 28th International Conference on Machine Learning, ICML 2011
JF - Proceedings of the 28th International Conference on Machine Learning, ICML 2011
T2 - 28th International Conference on Machine Learning, ICML 2011
Y2 - 28 June 2011 through 2 July 2011
ER -
ID: 301831085