MILEAGE: Multiple Instance LEArning with Global Embedding

Multiple Instance Learning (MIL) generally represents each example as a collection of instances such that the features for local objects can be better captured, whereas traditional methods typically extract a global feature vector for each example as an integral part. However, there is limited research work on investigating which of the two learning scenarios performs better. This paper proposes a novel framework - Multiple Instance LEArning with Global Embedding (MILEAGE), in which the global feature vectors for traditional learning methods are integrated into the MIL setting. Within the proposed framework, a large margin method is formulated to adaptively tune the weights on the two different kinds of feature representations (i.e., global and multiple instance) for each example and trains the classifier simultaneously. An extensive set of experiments are conducted to demonstrate the advantages of the proposed method.

[1]  Kristin P. Bennett,et al.  Fast Bundle Algorithm for Multiple-Instance Learning , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Jun Wang,et al.  Solving the Multiple-Instance Problem: A Lazy Learning Approach , 2000, ICML.

[3]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[4]  Thomas Gärtner,et al.  Multi-Instance Kernels , 2002, ICML.

[5]  Yixin Chen,et al.  MILES: Multiple-Instance Learning via Embedded Instance Selection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[8]  Alexander J. Smola,et al.  Bundle Methods for Regularized Risk Minimization , 2010, J. Mach. Learn. Res..

[9]  Jochem Zowe,et al.  A Version of the Bundle Idea for Minimizing a Nonsmooth Function: Conceptual Idea, Convergence Analysis, Numerical Results , 1992, SIAM J. Optim..

[10]  Alexander J. Smola,et al.  Bundle Methods for Machine Learning , 2007, NIPS.

[11]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[12]  Fernando De la Torre,et al.  Gaussian Processes Multiple Instance Learning , 2010, ICML.

[13]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[14]  Sally A. Goldman,et al.  MISSL: multiple-instance semi-supervised learning , 2006, ICML.

[15]  Mubarak Shah,et al.  Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[17]  Jun Gao,et al.  Identifying Multi-instance Outliers , 2010, SDM.

[18]  D. Noll Bundle Method for Non-Convex Minimization with Inexact Subgradients and Function Values , 2013 .

[19]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[20]  Nenghai Yu,et al.  Multiple-instance ranking: Learning to rank images for image retrieval , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Antonio Fuduli,et al.  Minimizing Nonconvex Nonsmooth Functions via Cutting Planes and Proximity Control , 2003, SIAM J. Optim..

[22]  Warren Hare,et al.  A Redistributed Proximal Bundle Method for Nonconvex Optimization , 2010, SIAM J. Optim..

[23]  Dan Zhang,et al.  Multiple Instance Learning on Structured Data , 2011, NIPS.

[24]  Michael W. Berry,et al.  Survey of Text Mining: Clustering, Classification, and Retrieval , 2007 .

[25]  Thierry Artières,et al.  Large margin training for hidden Markov models with partially observed states , 2009, ICML '09.

[26]  Zhouyu Fu,et al.  An instance selection approach to Multiple instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Alan L. Yuille,et al.  The Concave-Convex Procedure , 2003, Neural Computation.

[28]  Nello Cristianini,et al.  Composite Kernels for Hypertext Categorisation , 2001, ICML.

[29]  Zhi-Hua Zhou,et al.  Multi-instance learning by treating instances as non-I.I.D. samples , 2008, ICML '09.

[30]  Zhi-Hua Zhou,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2006, NIPS.

[31]  Mark Craven,et al.  Supervised versus multiple instance learning: an empirical comparison , 2005, ICML.

[32]  James R. Foulds,et al.  Revisiting Multiple-Instance Learning Via Embedded Instance Selection , 2008, Australasian Conference on Artificial Intelligence.