Correntropy based nonnegative matrix factorization : algorithms and clustering applications
In this thesis, to improve existing correntropy based nonnegative matrix factorization (NMF) algorithms and develop new methods for enlarging the range and enhancing the performance in clustering tasks, three novel correntropy based NMF algorithms are proposed, which are respectively the correntrop...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/145283 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-145283 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1452832023-07-04T15:02:01Z Correntropy based nonnegative matrix factorization : algorithms and clustering applications Peng, Siyuan Lin Zhiping Ser Wee School of Electrical and Electronic Engineering Centre for Bio Devices and Signal Analysis (VALENS) ewser@ntu.edu.sg, EZPLin@ntu.edu.sg Engineering::Electrical and electronic engineering In this thesis, to improve existing correntropy based nonnegative matrix factorization (NMF) algorithms and develop new methods for enlarging the range and enhancing the performance in clustering tasks, three novel correntropy based NMF algorithms are proposed, which are respectively the correntropy based graph regularized concept factorization algorithm, the correntropy based orthogonal nonnegative matrix tri-factorization algorithm, and the correntropy based semi-supervised nonnegative matrix factorization algorithm. The half-quadratic optimization technique is adopted to solve the optimization problems of the proposed algorithms, and the multiplicative update rules are derived. The new algorithms are analyzed from different aspects such as convergence, robustness, computational complexity, and relationships with several previous NMF methods. Experimental results demonstrate the effectiveness and robustness of the proposed algorithms for clustering applications on real world datasets compared with several state-of-the-art methods. Doctor of Philosophy 2020-12-16T08:20:16Z 2020-12-16T08:20:16Z 2020 Thesis-Doctor of Philosophy Peng, S. (2020). Correntropy based nonnegative matrix factorization : algorithms and clustering applications. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/145283 10.32657/10356/145283 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Electrical and electronic engineering |
spellingShingle |
Engineering::Electrical and electronic engineering Peng, Siyuan Correntropy based nonnegative matrix factorization : algorithms and clustering applications |
description |
In this thesis, to improve existing correntropy based nonnegative matrix factorization (NMF) algorithms and develop new methods for enlarging the range and enhancing the performance in clustering tasks,
three novel correntropy based NMF algorithms are proposed, which are respectively the correntropy based graph regularized concept factorization algorithm, the correntropy based orthogonal nonnegative matrix tri-factorization algorithm, and the correntropy based semi-supervised nonnegative matrix factorization algorithm. The half-quadratic optimization technique is adopted to solve the optimization problems of the proposed algorithms, and the multiplicative update rules are derived. The new algorithms are analyzed from different aspects such as convergence, robustness, computational complexity, and relationships with several previous NMF methods. Experimental results demonstrate the effectiveness and robustness of the proposed algorithms for clustering applications on real world datasets compared with several state-of-the-art methods. |
author2 |
Lin Zhiping |
author_facet |
Lin Zhiping Peng, Siyuan |
format |
Thesis-Doctor of Philosophy |
author |
Peng, Siyuan |
author_sort |
Peng, Siyuan |
title |
Correntropy based nonnegative matrix factorization : algorithms and clustering applications |
title_short |
Correntropy based nonnegative matrix factorization : algorithms and clustering applications |
title_full |
Correntropy based nonnegative matrix factorization : algorithms and clustering applications |
title_fullStr |
Correntropy based nonnegative matrix factorization : algorithms and clustering applications |
title_full_unstemmed |
Correntropy based nonnegative matrix factorization : algorithms and clustering applications |
title_sort |
correntropy based nonnegative matrix factorization : algorithms and clustering applications |
publisher |
Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/145283 |
_version_ |
1772829161608445952 |