Novelty Detection Under Multi-Label Multi-Instance Framework
Qi Lou, Raviv Raich, Forrest Briggs, Xiaoli Z. Fern

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 realworld
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.