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In this paper, we propose a fast mean-field method called LHMF to handle probabilistic models of large-scale data in high dimensional space. By using diffusion map locally linear embedding method which is a non-linear dimensionality reduction method, we first embed the high dimensional data into a low dimensional space. Then we construct a coarse-grained graph which preserves the spectral properties...
Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. An example application of MIML is visual object recognition in which each image is represented by multiple key points (i.e., instances) and is assigned to multiple object categories. In this paper, we study the problem of learning...
In this paper we propose a new distributed learning method called distributed network boosting (DNB) algorithm for distributed applications. The learned hypotheses are exchanged between neighboring sites during learning process. Theoretical analysis shows that the DNB algorithm minimizes the cost function through the collaborative functional gradient descent in hypotheses space. Comparison results...
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