Local dimensionality reduction for multiple instance learning
Saehoon Kim, Seungjin Choi · 2010
Multiple instance learning involves labeling bags (sets of instances) rather than individual instances. Positive bags contain both true positive and false positive instances, leading to label ambiguity, while negative bags consist of only true negative instances. Since labels for individual instances are not known, a direct application of existing discriminant analysis or dimensionality reduction methods often yields an undesirable projection direction due to this label ambiguity in positive bags. In this paper we present a citation local Fisher discriminant analysis (CLFDA) where we incorporate both citation and reference information into local Fisher discriminant analysis, in order to detect false positive instances whose corresponding labels are corrected to be negative. To our best knowledge, CLFDA is the first attempt in supervised dimensionality reduction for multiple instance learning. Numerical experiments on several benchmark datasets confirm that CLFDA outperforms existing methods in the task of multiple instance learning.