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We study content-based learning to rank from the perspective of learning distance functions. Standardly, the two key issues of learning to rank, feature mappings and score functions, are usually modeled separately, and the learning is usually restricted to modeling a linear distance function such as the Mahalanobis distance. However, the modeling of feature mappings and score functions are mutually...
Multi-view high-dimensional data become increasingly popular in the big data era. Feature selection is a useful technique for alleviating the curse of dimensionality in multi-view learning. In this paper, we study unsupervised feature selection for multi-view data, as class labels are usually expensive to obtain. Traditional feature selection methods are mostly designed for single-view data and cannot...
In this paper, we propose an online multi-view clustering algorithm, OMVC, which deals with large-scale incomplete views. We model the multi-view clustering problem as a joint weighted NMF problem and process the multi-view data chunk by chunk to reduce the memory requirement. OMVC learns the latent feature matrices for all the views and pushes them towards a consensus. We further increase the robustness...
One of the largest constituents of big data is the crowdsourced or user-generated data which contain a wide range of valuable information. However, they are inherently biased and possibly spammed, making trustworthy information extraction an imperative task. As a special case, we study reviewer-posted ratings for products. The biased ratings can lead to disappointed customers due to overrated products,...
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