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Spectral density of complex network can reflect network's structural properties. After the comparison of WS ‘small-world’ networks, ER random networks, BA ‘scale-free’ networks and CNN networks with different parameters and scales, the shapes of spectral density curves perform very strong clustering features. Based on these results, this paper proposes a novel method analyzing the similarity of network...
Spectral clustering algorithm has been demonstrated to be an effective unsupervised learning method. The spectral graph theory indicates that the eigenvalues and eigenvectors of the graph Laplacian are closely related with the clustering results. In this paper we prove that the distribution of the eigenvalues describes the distinctness of clusters and the eigenvectors implicitly present the target...
Feature extraction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. The representation extracted are often beneficial to mitigate the computational complexity and improve the accuracy of a particular classifier. In this paper we introduce a novel feature extraction algorithm called K nearest neighbor local margin maximization and apply it to...
Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selection, feature transformation (or feature projection) and projected clustering. Being widely used in many applications, these methods aim to capture global patterns and are typically performed in the full feature space. In...
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