Supervised aggregated feature learning for multiple instance classification

This paper introduces a novel algorithm, called Supervised Aggregated FEature learning or SAFE, which combines both (local) instance level and (global) bag level information in a joint framework to address the multiple instance classification task. In this realm, the collective assumption is used to express the relationship between the instance labels and the bag labels, by means of taking the sum as aggregation rule. The proposed model is formulated within a least squares support vector machine setting, where an unsupervised core model (either kernel PCA or kernel spectral clustering) at the instance level is combined with a classification loss function at the bag level. The corresponding dual problem consists of solving a linear system, and the bag classifier is obtained by aggregating the instance scores. Synthetic experiments suggest that SAFE is advantageous when the instances from both positive and negative bags can be naturally grouped in the same cluster. Moreover, real-life experiments indicate that SAFE is competitive with the best state-of-the-art methods.