Component recognition with three-dimensional fluorescence spectra based on non-negative matrix factorization

被引：8
作者：
Yu, Shaohui [1 ,2 ]
Zhang, Yujun [1 ]
Liu, Wenqing [1 ]
Zhao, Nanjing [1 ]
Xiao, Xue [1 ]
Yin, Gaofang [1 ]
机构：
[1] Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230031, Peoples R China
[2] Hefei Normal Univ, Dept Math, Hefei 230061, Peoples R China
来源：
JOURNAL OF CHEMOMETRICS | 2011年 / 25卷 / 11期
关键词：
non-negative matrix factorization; three-dimensional fluorescence spectra; integrated similarity index; component recognition; TRILINEAR DECOMPOSITION ALGORITHM; IDENTIFICATION;
D O I：
10.1002/cem.1404
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Non-negative matrix factorization (NMF) is a widely used approach in signal processing. In this work, we apply it to the component recognition of mixtures with multicomponent three-dimensional fluorescence spectra. Compared with the popular PARAFAC for component recognition, NMF has the following advantages: on one hand, the decomposed spectra are three dimensional, and thus, more information can be obtained, which is beneficial for component recognition; on the other hand, the decomposed spectra are non-negative and thus have a certain physical significance. More importantly, we propose a type of integrated similarity indices for the three-dimensional fluorescence spectra, which, by construction, is good at component recognition from overlapping fluorescence spectra. Experiment results demonstrate that NMF combined with integrated similarity index provides an effective method for component recognition of multicomponent three-dimensional overlapping fluorescence spectra. Copyright (C) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码：586 / 591
页数：6
相关论文
共 50 条
[21]   Deep Learning and Non-Negative Matrix Factorization in Recognition of Mammograms [J].
Swiderski, Bartosz ;
Kurek, Jaroslaw ;
Osowski, Stanislaw ;
Kruk, Michal ;
Barhoumi, Walid .
EIGHTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2016), 2017, 10225
[22]   Chinese Character Recognition Using Non-negative Matrix Factorization [J].
Voon, Chen Huey ;
Shin, Ker ;
Shean, Ng Wei .
JURNAL KEJURUTERAAN, 2024, 36 (02) :653-660
[23]   Speaker Clustering Based on Non-negative Matrix Factorization [J].
Nishida, Masafumi ;
Yamamoto, Seiichi .
12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5, 2011, :956-959
[24]   IMAGE PREDICTION BASED ON NON-NEGATIVE MATRIX FACTORIZATION [J].
Turkan, Mehmet ;
Guillemot, Christine .
2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2011, :789-792
[25]   Dropout non-negative matrix factorization [J].
Zhicheng He ;
Jie Liu ;
Caihua Liu ;
Yuan Wang ;
Airu Yin ;
Yalou Huang .
Knowledge and Information Systems, 2019, 60 :781-806
[26]   Non-negative Matrix Factorization: A Survey [J].
Gan, Jiangzhang ;
Liu, Tong ;
Li, Li ;
Zhang, Jilian .
COMPUTER JOURNAL, 2021, 64 (07) :1080-1092
[27]   Dropout non-negative matrix factorization [J].
He, Zhicheng ;
Liu, Jie ;
Liu, Caihua ;
Wang, Yuan ;
Yin, Airu ;
Huang, Yalou .
KNOWLEDGE AND INFORMATION SYSTEMS, 2019, 60 (02) :781-806
[28]   Non-negative matrix factorization with α-divergence [J].
Cichocki, Andrzej ;
Lee, Hyekyoung ;
Kim, Yong-Deok ;
Choi, Seungjin .
PATTERN RECOGNITION LETTERS, 2008, 29 (09) :1433-1440
[29]   CLUSTERING BY NON-NEGATIVE MATRIX FACTORIZATION WITH INDEPENDENT PRINCIPAL COMPONENT INITIALIZATION [J].
Gong, Liyun ;
Nandi, Asoke K. .
2013 PROCEEDINGS OF THE 21ST EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2013,
[30]   On affine non-negative matrix factorization [J].
Laurberg, Hans ;
Hansen, Lars Kai .
2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL II, PTS 1-3, 2007, :653-+
12345