Author
Abrol, V
Sharma, P
Journal title
Proceedings of the ICML 2020
Issue
2020
Volume
119
Last updated
2021-05-11T13:07:48.723+01:00
Page
42-51
Abstract
Archetypal analysis (AA) aims to extract patterns using self-expressive decomposition of data as convex combinations of extremal points (on the convex hull) of the data. This work presents a computationally efficient greedy AA (GAA) algorithm. GAA leverages the underlying geometry and sparseness property of AA, is scalable to larger datasets, and has significantly faster convergence to existing methods. To achieve this, archetypes are learned via sparse projection of data in linearly transformed space. GAA employs an iterative subset selection approach to identify archetypes based on the sparsity of convex representations. The work further presents the use of GAA algorithm for extended AA models such as robust and kernel AA. Experimental results show that GAA is significantly faster while performing comparable to existing methods for tasks such as classification, data visualization/categorization.
Symplectic ID
1117052
Publication type
Conference Paper
Publication date
21 November 2020
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