title: Online semi-supervised growing neural gas.
abstract: In this paper we introduce online semi-supervised growing neural gas (OSSGNG), a novel online semi-supervised classification approach based on growing neural gas (GNG). Existing semi-supervised classification approaches based on GNG require that the training data is explicitly stored as the labeling is performed a posteriori after the training phase. As main contribution, we present an approach that relies on online labeling and prediction functions to process labeled and unlabeled data uniformly and in an online fashion, without the need to store any of the training examples explicitly. We show that using on-the-fly labeling strategies does not significantly deteriorate the performance of classifiers based on GNG, while circumventing the need to explicitly store training examples. Armed with this result, we then present a semi-supervised extension of GNG (OSSGNG) that relies on the above mentioned online labeling functions to label unlabeled examples and incorporate them into the model on-the-fly. As an important result, we show that OSSGNG performs as good as previous semi-supervised extensions of GNG which rely on offline labeling strategies. We also show that OSSGNG compares favorably to other state-of-the-art semi-supervised learning approaches on standard benchmarking datasets.
authors.affiliation: Semantic Computing Group, CITEC, Bielefeld University, Bielefeld, Germany. obeyer@cit-ec.uni-bielefeld.de
authors.forename: Oliver
authors.initials: O
authors.lastname: Beyer
authors.forename: Philipp
authors.initials: P
authors.lastname: Cimiano
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification / Q000379:methods; D003198:Computer Simulation; D016208:Databases, Factual; D016571:Neural Networks, Computer; D009862:Online Systems