A fisher score-based multi-instance learning method assisted by mixture of factor analysis

Abstract

Multi-instance learning (MIL) is a problem with significant importance due to the existence of ambiguous labels in many practical tasks. The training data in MIL problem is represented by labeled bags containing a various number of unlabeled instances. This inexact label information has brought complexities to the classification of instances and bags. Fisher scores have been utilized to extract fixed-length representation vectors from the MIL bags with the assistance of generative models. However, the generative model utilized by the existing methods might disregard the covariance information. In this paper, we propose a multi-instance learning method (miMFA) utilizing Fisher scores derived from the mixture of factor analysis (MFA) model. The MFA generates nonlinear data with a set of local factor analysis models, while each local model approximates the full covariance Gaussian using latent factors. Thus, the MFA could cover the data distribution and generate Fisher scores effectively. The MFA-based Fisher score is then utilized to form the bag representation. Moreover, a reinforcement vector and the complementary information from auxiliary MIL methods are introduced for performance refinement. The proposed miMFA is evaluated on 11 MIL datasets, and achieves satisfactory results compared with the existing methods.