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NIPS
2007

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

10 years 28 days ago
Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data
Content-based image suggestion (CBIS) targets the recommendation of products based on user preferences on the visual content of images. In this paper, we motivate both feature selection and model order identification 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 using 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.
Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NIPS
Authors Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila
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