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Activity Number:
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336
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Type:
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Invited
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Date/Time:
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Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #300194 |
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Title:
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Modal Inference and Its Application to High-Dimensional Clustering
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Author(s):
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Surajit Ray*+
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Companies:
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Boston University
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Address:
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111 Cummington Street RM 222, Boston, MA, 02215,
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Keywords:
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Clustering ; Modal Clustering ; Topography ; Modal EM ; Multiscale
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Abstract:
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Ray and Lindsay (2005) show that the topography of multivariate mixtures, in the sense of their key features as a density, can be analyzed rigorously in lower dimensions by use of a ridgeline manifold that contains all critical points as well as the ridges of the density. To use this rich feature for data analysis we first construct an extension of EM algorithm that can be used to find the modes of a mixture density. Even in very high dimensions the computational complexity of our EM algorithm is extremely low. These tools can be used in various ways. For one, we can take a conventional mixture analysis and cluster together those components whose contribution is actually unimodal. We can also turn kernel density estimation into clustering tool in which the data points become identified with each other by their association with a common mode of the density estimator.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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