ICA-BASED ACCELERATION OF PROBABILISTIC LATENT COMPONENT ANALYSIS FOR MASS SPECTROMETRY-BASED EXPLOSIVES DETECTION

被引:0
|
作者
Kawaguchi, Yohei [1 ]
Togami, Masahito [1 ]
Nagano, Hisashi [1 ]
Hashimoto, Yuichiro [1 ]
Sugiyama, Masuyuki [1 ]
Takada, Yasuaki [1 ]
机构
[1] Hitachi Ltd, Cent Res Lab, Tokyo 1858601, Japan
关键词
Mass spectrometry; Blind source separation; Probabilistic latent component analysis (PLCA); Independent component analysis (ICA); Sparsity;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
We propose a new method to separate mass spectra into components of each chemical compound for explosives detection. The conventional method based on probabilistic latent component analysis (PLCA) is effective because the method can solve the problems of non-negativity and non-orthogonality by using sparsity of the domain of explosives detection. However, the convergence of the method is slow. and the calculation time is long. In order to solve this problem. the proposed method makes use of independent component analysis (ICA) in the initialization process. Experimental results indicate that the convergence of the proposed method is accelerated, and total calculation time is decreased.
引用
收藏
页码:2795 / 2799
页数:5
相关论文
共 50 条
  • [1] Separation of Mass Spectra Based on Probabilistic Latent Component Analysis for Explosives Detection
    Kawaguchi, Yohei
    Togami, Masahito
    Nagano, Hisashi
    Hashimoto, Yuichiro
    Sugiyama, Masuyuki
    Takada, Yasuaki
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2015, E98A (09) : 1888 - 1897
  • [2] MASS SPECTRA SEPARATION FOR EXPLOSIVES DETECTION BY USING PROBABILISTIC LATENT COMPONENT ANALYSIS
    Kawaguchi, Yohei
    Togami, Masahito
    Nagano, Hisashi
    Hashimoto, Yuichiro
    Sugiyama, Masuyuki
    Takada, Yasuaki
    2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, : 1665 - 1668
  • [3] ICA-based probabilistic local appearance models
    Zhou, XS
    Moghaddam, B
    Huang, TS
    2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL I, PROCEEDINGS, 2001, : 161 - 164
  • [4] Mass spectrometry-based fecal metabolome analysis
    Xu, Jing
    Zhang, Qin-Feng
    Zheng, Jie
    Yuan, Bi-Feng
    Feng, Yu-Qi
    TRAC-TRENDS IN ANALYTICAL CHEMISTRY, 2019, 112 : 161 - 174
  • [5] Mass spectrometry-based lipid analysis and imaging
    Pathmasiri, Koralege C.
    Nguyen, Thu T. A.
    Khamidova, Nigina
    Cologna, Stephanie M.
    NEW METHODS AND SENSORS FOR MEMBRANE AND CELL VOLUME RESEARCH, 2021, 88 : 315 - 357
  • [6] ICA-based image analysis for robot vision
    Ohnishi, Naoya
    Imiya, Atsushi
    INDEPENDENT COMPONENT ANALYSIS AND SIGNAL SEPARATION, PROCEEDINGS, 2007, 4666 : 754 - +
  • [7] INFORMATICS FOR MASS SPECTROMETRY-BASED RNA ANALYSIS
    Nakayama, Hiroshi
    Takahashi, Nobuhiro
    Isobe, Toshiaki
    MASS SPECTROMETRY REVIEWS, 2011, 30 (06) : 1000 - 1012
  • [8] Mass spectrometry-based approaches to metabolomic analysis
    Geiger, T.
    FEBS JOURNAL, 2017, 284 : 67 - 67
  • [9] Mass spectrometry-based proteornics for the detection of plant pathogens
    Padliya, Neerav D.
    Cooper, Bret
    PROTEOMICS, 2006, 6 (14) : 4069 - 4075
  • [10] ICA-based Method for Power Quality Disturbance Detection
    Nagata, Erick Akio
    Ferreira, Danton Diego
    Duque, Carlos Augusto
    PROCEEDINGS OF 2016 17TH INTERNATIONAL CONFERENCE ON HARMONICS AND QUALITY OF POWER (ICHQP), 2016, : 412 - 417