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Based on the advantages of data smoothing, this paper presents a new collaborative filtering algorithm to solve the problem of data sparsity in recommender system. The key innovation of the algorithm consists of clustering and data smoothing. Clustering is used to find out the similarity between users. This paper adopts a new method to cluster users. Data smoothing is designed to solve the problem...
In this paper, we propose Two-stage Patch-based Sparse Multi-value Descriptor (TPSMD), a generalization of Sparse Linear Regression Binary method. The TPSMD makes two contributions. First, the multi-value strategy introduces user-specified parameters to improve the binarization, which makes our method more discriminant and less sensitive to noise. The multi-value strategy is a comprise between the...
This paper illustrates that elaborately selecting some discriminative patches instead of using all patches is helpful for recognition tasks. We propose an effective method called bagging greedy algorithm (BGA) for choosing a collection of patches that is discriminative in terms of classification. Compared with greedy algorithm, BGA is less likely to get into local optimum. Based on the subset of patches,...
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