Efficient Higher-Order Clustering on the Grassmann Manifold

Jain, Suraj and Govindu, Venu Madhav (2013) Efficient Higher-Order Clustering on the Grassmann Manifold. In: IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), DEC 01-08, 2013, Sydney, AUSTRALIA, pp. 3511-3518.

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Abstract

The higher-order clustering problem arises when data is drawn from multiple subspaces or when observations fit a higher-order parametric model. Most solutions to this problem either decompose higher-order similarity measures for use in spectral clustering or explicitly use low-rank matrix representations. In this paper we present our approach of Sparse Grassmann Clustering (SGC) that combines attributes of both categories. While we decompose the higher-order similarity tensor, we cluster data by directly finding a low dimensional representation without explicitly building a similarity matrix. By exploiting recent advances in online estimation on the Grassmann manifold (GROUSE) we develop an efficient and accurate algorithm that works with individual columns of similarities o 4th r partial observations thereof. Since it avoids the storage and decomposition of large similarity matrices, our method is efficient, scalable and has low memory requirements even for large-scale data. We demonstrate the performance of our SGC method on a variety of segmentation problems including planar segmentation of Kinect depth maps and motion segmentation of the Hopkins 155 dataset for which we achieve performance comparable to the state-of-the-art.

Item Type:	Conference Proceedings
Series.:	IEEE International Conference on Computer Vision
Publisher:	IEEE
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	25 Aug 2016 10:39
Last Modified:	25 Aug 2016 10:39
URI:	http://eprints.iisc.ac.in/id/eprint/54311

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