Graduate Project
 

Novelty Detection Under the Multi-Instance Multi-Label Framework

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https://ir.library.oregonstate.edu/concern/graduate_projects/2f75rc84g

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  • Novelty detection plays an important role in machine learning and signal processing. This project studies novelty detection in a new setting where the data object is represented as a bag of instances and associated with multiple class labels, referred to as multi-instance multi-label (MIML) learning. Contrary to the common assumption in MIML that each instance in a bag belongs to one of the known classes, in novelty detection, we focus on the scenario where bags may contain novel-class instances. The goal is to determine, for any given instance in a new bag, whether it belongs to a known class or a novel class. Detecting novelty in the MIML setting captures many real-world phenomena and has many potential applications. For example, in a collection of tagged images, the tag may only cover a subset of objects existing in the images. Discovering an object whose class has not been previously tagged can be useful for the purpose of soliciting a label for the new object class. To address this novel problem, we present a discriminative framework for detecting new class instances. Experiments demonstrate the effectiveness of our proposed method, and reveal that the presence of unlabeled novel instances in training bags is helpful to the detection of such instances in testing stage. To the best of our knowledge, novelty detection in the MIML setting has not been investigated. Our main contributions are: (i) We propose a new problem – novelty detection in the MIML setting. (ii) We offer a framework based on score functions to solve the problem. (iii) We illustrate the efficacy of our method on a real-world MIML bioacoustics data.
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