Subspace-dependent adjacency matrix design via discrete-continuous optimization

Katsuya Hotta; Shenglin Mu; Yan Zhao; Chao Zhang

doi:10.1109/GCCE50665.2020.9291831

Subspace-dependent adjacency matrix design via discrete-continuous optimization

Katsuya Hotta, Shenglin Mu, Yan Zhao, Chao Zhang

Intellectual Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present a method for adjacency matrix design by applying both discrete and continuous optimization techniques, which are powered by energy minimization and gradient descent respectively. Most of the subspace clustering methods design the adjacency matrix by adopting a common calculation rule over the entire data set, which is under the assumption that a same noise model is shared between points. Therefore, these methods tend to fail when the points contain multi-source noises. To relax this limitation, we introduce a method that finds the neighborhoods by considering discrete-continuous optimization. Our method outperforms competitive methods on both synthetic and real-world data.

Original language	English
Title of host publication	2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	109-110
Number of pages	2
ISBN (Electronic)	9781728198026
DOIs	https://doi.org/10.1109/GCCE50665.2020.9291831
State	Published - 2020/10/13
Event	9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan Duration: 2020/10/13 → 2020/10/16

Publication series

Name	2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference	9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Country/Territory	Japan
City	Kobe
Period	2020/10/13 → 2020/10/16

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Media Technology
Instrumentation
Computer Networks and Communications
Computer Vision and Pattern Recognition

Access to Document

10.1109/GCCE50665.2020.9291831

Cite this

Hotta, K., Mu, S., Zhao, Y., & Zhang, C. (2020). Subspace-dependent adjacency matrix design via discrete-continuous optimization. In 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 (pp. 109-110). Article 9291831 (2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE50665.2020.9291831

@inproceedings{6d7fbbed121e444bbd98c8e2e436f599,

title = "Subspace-dependent adjacency matrix design via discrete-continuous optimization",

abstract = "We present a method for adjacency matrix design by applying both discrete and continuous optimization techniques, which are powered by energy minimization and gradient descent respectively. Most of the subspace clustering methods design the adjacency matrix by adopting a common calculation rule over the entire data set, which is under the assumption that a same noise model is shared between points. Therefore, these methods tend to fail when the points contain multi-source noises. To relax this limitation, we introduce a method that finds the neighborhoods by considering discrete-continuous optimization. Our method outperforms competitive methods on both synthetic and real-world data.",

author = "Katsuya Hotta and Shenglin Mu and Yan Zhao and Chao Zhang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 9th IEEE Global Conference on Consumer Electronics, GCCE 2020 ; Conference date: 13-10-2020 Through 16-10-2020",

year = "2020",

month = oct,

day = "13",

doi = "10.1109/GCCE50665.2020.9291831",

language = "英語",

series = "2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "109--110",

booktitle = "2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020",

}

Hotta, K, Mu, S, Zhao, Y & Zhang, C 2020, Subspace-dependent adjacency matrix design via discrete-continuous optimization. in 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020., 9291831, 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 109-110, 9th IEEE Global Conference on Consumer Electronics, GCCE 2020, Kobe, Japan, 2020/10/13. https://doi.org/10.1109/GCCE50665.2020.9291831

Subspace-dependent adjacency matrix design via discrete-continuous optimization. / Hotta, Katsuya; Mu, Shenglin; Zhao, Yan et al.
2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 109-110 9291831 (2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Subspace-dependent adjacency matrix design via discrete-continuous optimization

AU - Hotta, Katsuya

AU - Mu, Shenglin

AU - Zhao, Yan

AU - Zhang, Chao

PY - 2020/10/13

Y1 - 2020/10/13

N2 - We present a method for adjacency matrix design by applying both discrete and continuous optimization techniques, which are powered by energy minimization and gradient descent respectively. Most of the subspace clustering methods design the adjacency matrix by adopting a common calculation rule over the entire data set, which is under the assumption that a same noise model is shared between points. Therefore, these methods tend to fail when the points contain multi-source noises. To relax this limitation, we introduce a method that finds the neighborhoods by considering discrete-continuous optimization. Our method outperforms competitive methods on both synthetic and real-world data.

AB - We present a method for adjacency matrix design by applying both discrete and continuous optimization techniques, which are powered by energy minimization and gradient descent respectively. Most of the subspace clustering methods design the adjacency matrix by adopting a common calculation rule over the entire data set, which is under the assumption that a same noise model is shared between points. Therefore, these methods tend to fail when the points contain multi-source noises. To relax this limitation, we introduce a method that finds the neighborhoods by considering discrete-continuous optimization. Our method outperforms competitive methods on both synthetic and real-world data.

UR - http://www.scopus.com/inward/record.url?scp=85099337083&partnerID=8YFLogxK

U2 - 10.1109/GCCE50665.2020.9291831

DO - 10.1109/GCCE50665.2020.9291831

M3 - 会議への寄与

AN - SCOPUS:85099337083

T3 - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

SP - 109

EP - 110

BT - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE Global Conference on Consumer Electronics, GCCE 2020

Y2 - 13 October 2020 through 16 October 2020

ER -

Hotta K, Mu S, Zhao Y, Zhang C. Subspace-dependent adjacency matrix design via discrete-continuous optimization. In 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 109-110. 9291831. (2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020). doi: 10.1109/GCCE50665.2020.9291831

Subspace-dependent adjacency matrix design via discrete-continuous optimization

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this