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A two-stage classification method for high-dimensional data and point clouds

A two-stage classification method for high-dimensional data and point clouds
A two-stage classification method for high-dimensional data and point clouds
High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a fuzzy classification method such as the standard support vector machine is used to generate a warm initialization. We then apply a two-stage approach named SaT (smoothing and thresholding) to improve the classification. In the first stage, an unconstraint convex variational model is implemented to purify and smooth the initialization, followed by the second stage which is to project the smoothed partition obtained at stage one to a binary partition. These two stages can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing stage has a unique solution and can be solved by a specifically designed primal-dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. The experimental results demonstrate clearly that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.
math.NA, cs.CV
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond
9185af9b-f073-4e3d-8f22-1dac8d28db58
Xie, Xiaoyu
3923dab9-5693-49c5-a6f4-e0d79704b4df
Zeng, Tieyong
8bae04dd-2c0d-49f2-898b-30cdc0f5e286
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond
9185af9b-f073-4e3d-8f22-1dac8d28db58
Xie, Xiaoyu
3923dab9-5693-49c5-a6f4-e0d79704b4df
Zeng, Tieyong
8bae04dd-2c0d-49f2-898b-30cdc0f5e286

Cai, Xiaohao, Chan, Raymond, Xie, Xiaoyu and Zeng, Tieyong (2019) A two-stage classification method for high-dimensional data and point clouds. arXiv.

Record type: Article

Abstract

High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a fuzzy classification method such as the standard support vector machine is used to generate a warm initialization. We then apply a two-stage approach named SaT (smoothing and thresholding) to improve the classification. In the first stage, an unconstraint convex variational model is implemented to purify and smooth the initialization, followed by the second stage which is to project the smoothed partition obtained at stage one to a binary partition. These two stages can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing stage has a unique solution and can be solved by a specifically designed primal-dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. The experimental results demonstrate clearly that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.

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1905.08538v1
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More information

Published date: 21 May 2019
Additional Information: 21 pages, 4 figures
Keywords: math.NA, cs.CV

Identifiers

Local EPrints ID: 438775
URI: http://eprints.soton.ac.uk/id/eprint/438775
PURE UUID: 7921b6ca-78a7-44cc-823a-53f7b9c74c86

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Date deposited: 24 Mar 2020 17:30
Last modified: 13 Jul 2020 16:39

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Contributors

Author: Xiaohao Cai
Author: Raymond Chan
Author: Xiaoyu Xie
Author: Tieyong Zeng

University divisions

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