Due to the small size of available labeled data for semi- supervised learning, approaches to this problem make strong assump- tions about the data, performing well only when such assumptions hold true. However, a lot of effort may have to be spent in understanding the data so that the most suitable model can be applied. This process can be as critical as gathering labeled data. One way to overcome this hindrance is to control the contribution of different assumptions to the model, rendering it capable of performing reasonably in a wide range of applications. In this paper we propose a collective matrix factorization model that simultaneously decomposes the predictor, neighborhood and target matrices (PNT-CMF) to achieve semi-supervised classification. By controlling how strongly the model relies on different assumptions, PNT- CMF is able to perform well on a wider variety of datasets. Experiments on synthetic and real world datasets show that, while state-of-the-art models (TSVM and LapSVM) excel on datasets that match their char- acteristics and have a performance drop on the others, our approach outperforms them being consistently competitive in different situations.
['Lucas Drumond', 'Lars Schmidt-Thieme', 'Christoph Freudenthaler', 'Artus Krohn-Grimberghe']
Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification