Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila


Content-based image suggestion (CBIS) targets the recommendation of products based on user preferences on the visual content of images. In this paper, we mo- tivate both feature selection and model order identiļ¬cation as two key issues for a successful CBIS. We propose a generative model in which the visual features and users are clustered into separate classes. We identify the number of both user and image classes with the simultaneous selection of relevant visual features us- ing the message length approach. The goal is to ensure an accurate prediction of ratings for multidimensional non-Gaussian and continuous image descriptors. Experiments on a collected data have demonstrated the merits of our approach.