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Aggregate outputs learning is a newly proposed setting in data mining and machine learning. It differs from the classical supervised learning setting in that, training samples are packed into bags with only the aggregate outputs (labels for classification or real values for regression) provided. This problem is associated with several kinds of application background. We focus on the aggregate outputs...
This paper presents a simple but powerful extension of the maximum margin clustering (MMC) algorithm that optimizes multivariate performance measure specifically defined for clustering, including normalized mutual information, rand index and F-measure. Different from previous MMC algorithms that always employ the error rate as the loss function, our formulation involves a multivariate loss function...
Compressed sensing has received much attention in both data mining and signal processing communities. In this paper, we provide theoretical results to show that compressed spectral clustering, separating data samples into different clusters directly in the compressed measurement domain, is possible. Specifically, we provide theoretical bounds guaranteeing that if the data is measured directly in the...
Feature selection plays a fundamental role in many pattern recognition problems. However, most efforts have been focused on the supervised scenario, while unsupervised feature selection remains as a rarely touched research topic. In this paper, we propose manifold-based maximum margin feature selection (M3FS) to select the most discriminative features for clustering. M3FS targets to find those features...
Clustering is an old research topic in data mining and machine learning. Most of the traditional clustering methods can be categorized as local or global ones. In this paper, a novel clustering method that can explore both the local and global information in the data set is proposed. The method, Clustering with Local and Global Regularization (CLGR), aims to minimize a cost function that properly...
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