Graph-Regularized Generalized Low-Rank Models

Mihir Paradkar, Madeleine Udell; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 7-12

Image data is frequently extremely large and oftentimes pixel values are occluded or observed with noise. Additionally, images can be related to each other, as in images of a particular individual. This method augments the recently proposed Generalized Low Rank Model (GLRM) framework with graph regularization, which flexibly models relationships between images. For example, relationships might include images that change slowly over time (as in video or surveillance data), images of the same individual, or diagnostic images which picture the same medical condition. This paper proposes a fast optimization method to solve these graph-regularized GLRMs, which we have released as an open-source software library. We demonstrate that the method outperforms competing methods on a variety of data sets, and show how to use this method to classify and group images.