MILES: Multiple-Instance Learning via Embedded Instance Selection

• Published in: IEEE transactions on pattern analysis and machine intelligence 2006-12, Vol.28 (12), p.1931-1947
• Main Authors: Yixin Chen, Jinbo Bi, Wang, J.Z.
• Format: Article
• Language: English
• Subjects: 1-norm support vector machine; Algorithms; Application software; Applied sciences; Artificial Intelligence; Bags; Computer science; control theory; systems; Computer vision; drug activity prediction; Drugs; Exact sciences and technology; feature subset selection; image categorization; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Intelligence; Labeling; Labels; Learning; Learning systems; Multiple-instance learning; object recognition; Pattern analysis; Pattern recognition. Digital image processing. Computational geometry; Reproducibility of Results; Robustness; Sensitivity and Specificity; Similarity; Studies; Supervised learning; Support vector machine classification; Support vector machines; Training; Uncertainty
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2006.248

Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (multiple-instance learning via embedded instance selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence, 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty