A weakly supervised learning based clustering framework is proposed in this paper. As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count ( @math ), which is the number of unique classes among all instances inside the bag. In this task, no annotations on individual instances inside the bag are needed during training of the models. We mathematically prove that a perfect @math classifier, in principle, can be used to perfectly cluster individual instances inside the bags. In other words, perfect clustering of individual instances is possible even when no annotations on individual instances are given during training. We have constructed a neural network based @math classifier and experimentally shown that the clustering performance of our framework with our @math classifier is comparable to that of fully supervised learning models. We have also observed that our @math classifiers can potentially be used for zero-shot learning as they learn better semantic features than fully supervised models for unseen classes', which have never been input into the models during training.
MIL algorithms are grouped into three: , and , in which each bag is transformed to a feature vector and discriminative classifier trained on feature vectors in embedded space @cite_2 . Our @math model also employs embedded-space paradigm such that each bag embedding is done by using KDE on feature vectors of instances inside the bag, which are extracted by a neural network.
@cite_2
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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