  Novelty detection plays an important role in machine learning and signal
processing. This paper 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.
