Embedding Multiple Manifolds
نویسنده
چکیده
Many high dimensional data sets are characterized by a small number of degrees of freedom. Standard manifold-based dimensionality reduction techniques include the bias that all data lies on (or near) a single global manifold. Here we consider data sampled from multiple manifolds. We demonstrate a straightforward technique for multiple manifold embedding: the data is clustered, and each cluster is embedded separately. We show that standard (1-manifold) embedding techniques are ineffective for this problem, and that multiple-manifold embedding can accurately model this data. Single and multiple manifold embedding are compared on synthetic datasets, and a real-world image dataset.
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تاریخ انتشار 2005