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clustering [2017/04/11 13:57]
127.0.0.1 external edit
clustering [2018/03/15 11:40] (current)
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 Spectral clustering is a powerful clustering algorithm that suffers from high computational complexity, due to eigen decomposition. ​ We present a definition of the Laplacian matrix of the graph that enable us to perform eigen decomposition efficiently,​ using a deep autoencoder. The overall complexity of the algorithm for eigen decomposition is O(np), where n is the number of data points and p is the number of landmarks. At last, we evaluate the performance of the algorithm in different experiments. Spectral clustering is a powerful clustering algorithm that suffers from high computational complexity, due to eigen decomposition. ​ We present a definition of the Laplacian matrix of the graph that enable us to perform eigen decomposition efficiently,​ using a deep autoencoder. The overall complexity of the algorithm for eigen decomposition is O(np), where n is the number of data points and p is the number of landmarks. At last, we evaluate the performance of the algorithm in different experiments.
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 +https://​arxiv.org/​abs/​1803.00156v1 Autoencoding topology
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 +The problem of learning a manifold structure on a dataset is framed in terms of a generative model, to which we use ideas behind autoencoders (namely adversarial/​Wasserstein autoencoders) to fit deep neural networks. From a machine learning perspective,​ the resulting structure, an atlas of a manifold, may be viewed as a combination of dimensionality reduction and "​fuzzy"​ clustering.