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Journal articles on the topic 'Independent component analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Unnisa, Yaseen, Danh Tran, and Fu Chun Huang. "Statistical Independence and Independent Component Analysis." Applied Mechanics and Materials 553 (May 2014): 564–69. http://dx.doi.org/10.4028/www.scientific.net/amm.553.564.

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Independent Component Analysis (ICA) is a recent method of blind source separation, it has been employed in medical image processing and structural damge detection. It can extract source signals and the unmixing matrix of the system using mixture signals only. This novel method relies on the assumption that source signals are statistically independent. This paper looks at various measures of statistical independence (SI) employed in ICA, the measures proposed by Bakirov and his associates, and the effects of levels of SI of source signals on the output of ICA. Firstly, two statistical independ

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2

Kemp, Freda. "Independent Component Analysis Independent Component Analysis: Principles and Practice." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 3 (2003): 412. http://dx.doi.org/10.1111/1467-9884.00369_14.

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3

KAWAMOTO, Mitsuru. "Independent Component Analysis." Journal of Japan Society for Fuzzy Theory and Systems 11, no. 5 (1999): 759–62. http://dx.doi.org/10.3156/jfuzzy.11.5_55.

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4

Sztemberg-Lewandowska, Mirosława. "INDEPENDENT COMPONENT ANALYSIS." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, no. 468 (2017): 222–29. http://dx.doi.org/10.15611/pn.2017.468.23.

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5

Fearn, Tom. "Independent Component Analysis." NIR news 19, no. 3 (2008): 13–14. http://dx.doi.org/10.1255/nirn.1073.

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6

Hong, Sung Ee. "Exploring Independent Component Analysis Based on Ball Covariance." Korean Data Analysis Society 21, no. 6 (2019): 2721–35. http://dx.doi.org/10.37727/jkdas.2019.21.6.2721.

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7

Nordhausen, Joni Oja. "UNRAVELING INDEPENDENT COMPONENT ANALYSIS FOR TENSOR-VALUED DATA." Global Multidisciplinary Journal 02, no. 03 (2023): 01–07. http://dx.doi.org/10.55640/gmj-abc114.

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In the realm of data analysis, the exploration of independent component analysis (ICA) for tensor-valued data represents a burgeoning area of research. Unlike traditional scalar or vector data, tensor-valued data capture complex relationships and structures across multiple dimensions. Independent component analysis offers a powerful framework for decomposing tensor-valued data into statistically independent components, revealing underlying patterns and dependencies that may remain obscured in raw data representations. This paper delves into the application of ICA techniques specifically tailor

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8

Liu, Thomas T., Karla L. Miller, Eric C. Wong, Lawrence R. Frank, and Richard B. Buxton. "Identifying meaningful components in independent component analysis." NeuroImage 11, no. 5 (2000): S652. http://dx.doi.org/10.1016/s1053-8119(00)91582-9.

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9

Hyvärinen, Aapo, Patrik O. Hoyer, and Mika Inki. "Topographic Independent Component Analysis." Neural Computation 13, no. 7 (2001): 1527–58. http://dx.doi.org/10.1162/089976601750264992.

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In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated “independent” components are often not at all independent. We propose that this residual dependence structure could be used to define a topo-graphic order for the components. In particular, a distance between two components could be defined using their higher-order correlations, and this distance could be used to create a topographic representation. Thus, we obtain a linear decomposition int

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10

Miettinen, Jari, Markus Matilainen, Klaus Nordhausen, and Sara Taskinen. "Extracting Conditionally Heteroskedastic Components using Independent Component Analysis." Journal of Time Series Analysis 41, no. 2 (2019): 293–311. http://dx.doi.org/10.1111/jtsa.12505.

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11

JENTZSCH, INES. "INDEPENDENT COMPONENT ANALYSIS SEPARATES SEQUENCE-SENSITIVE ERP COMPONENTS." International Journal of Bifurcation and Chaos 14, no. 02 (2004): 667–78. http://dx.doi.org/10.1142/s0218127404009363.

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Human performance is strongly influenced by the sequence of events. Decreasing the response-stimulus interval (RSI) between events qualitatively changes these so-called sequential effects. Using event-related brain potentials (ERPs) to detect electrical brain activity related to sequential patterns helps to uncover mechanisms underlying the observed performance data. Using a spatial compatible two-choice task ERPs were recorded from 32 electrode sites and Independent Component Analysis (ICA) applied to separate sequence-sensitive ERP components from two experiments, involving different RSIs. I

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12

Samarov, Alexander, and Alexandre Tsybakov. "Nonparametric independent component analysis." Bernoulli 10, no. 4 (2004): 565–82. http://dx.doi.org/10.3150/bj/1093265630.

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13

Kawaguchi, Atsushi, and Young K. Truong. "LOGSPLINE INDEPENDENT COMPONENT ANALYSIS." Bulletin of informatics and cybernetics 43 (December 2011): 83–94. http://dx.doi.org/10.5109/1434313.

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14

Jie Luo, Bo Hu, Xie-Ting Ling, and Ruey-Wen Liu. "Principal independent component analysis." IEEE Transactions on Neural Networks 10, no. 4 (1999): 912–17. http://dx.doi.org/10.1109/72.774259.

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15

Honório, Bruno César Zanardo, Alexandre Cruz Sanchetta, Emilson Pereira Leite, and Alexandre Campane Vidal. "Independent component spectral analysis." Interpretation 2, no. 1 (2014): SA21—SA29. http://dx.doi.org/10.1190/int-2013-0074.1.

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Spectral decomposition techniques can break down the broadband seismic records into a series of frequency components that are useful for seismic interpretation and reservoir characterization. However, it is laborious and time-consuming to analyze and to interpret each seismic frequency volume taking all the usable seismic bandwidth. In this context, we propose a multivariate technique based on independent component analysis (ICA) with the goal of choosing the spectral components that best represent the whole seismic spectrum while keeping the main geological information. The ICA-based method g

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16

Chen, Aiyou, and Peter J. Bickel. "Efficient independent component analysis." Annals of Statistics 34, no. 6 (2006): 2825–55. http://dx.doi.org/10.1214/009053606000000939.

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17

Dhir, C. S., and Soo-Young Lee. "Discriminant Independent Component Analysis." IEEE Transactions on Neural Networks 22, no. 6 (2011): 845–57. http://dx.doi.org/10.1109/tnn.2011.2122266.

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18

Singer, A. "Spectral independent component analysis." Applied and Computational Harmonic Analysis 21, no. 1 (2006): 135–44. http://dx.doi.org/10.1016/j.acha.2006.03.003.

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19

Jing, Shuangxi, Qi Liu, Chenxu Luo, and Penghui Shi. "Comparison study of fast independent component analysis and constrained independent component analysis." Vibroengineering PROCEDIA 20 (October 19, 2018): 57–63. http://dx.doi.org/10.21595/vp.2018.20089.

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20

Hyvärinen, Aapo. "Independent component analysis: recent advances." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1984 (2013): 20110534. http://dx.doi.org/10.1098/rsta.2011.0534.

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Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some rec

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21

Leite, I. C. C., T. Sáfadi, and M. L. M. Carvalho. "Evaluation of seed radiographic images by independent component analysis and discriminant analysis." Seed Science and Technology 41, no. 2 (2013): 235–44. http://dx.doi.org/10.15258/sst.2013.41.2.06.

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22

HYVÄRINEN, AAPO, and ERKKI OJA. "SIMPLE NEURON MODELS FOR INDEPENDENT COMPONENT ANALYSIS." International Journal of Neural Systems 07, no. 06 (1996): 671–87. http://dx.doi.org/10.1142/s0129065796000646.

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Recently, several neural algorithms have been introduced for Independent Component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbian-like learning rules are introduced for estimating one of the independent components from sphered data. Some of the learning rules can be used to estimate an independent component which has a negative kurtosis, and the others estimate a component of positive kurtosis. Next, a two-unit system is introduced to estimate an independent component of any kurtosis. The results are then generalized to estimate independ

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23

Fouda, Mohammed E., Emre Neftci, Ahmed Eltawil, and Fadi Kurdahi. "Independent Component Analysis Using RRAMs." IEEE Transactions on Nanotechnology 18 (2019): 611–15. http://dx.doi.org/10.1109/tnano.2018.2880734.

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24

Gepshtein, Shai, and Yosi Keller. "Iterative spectral independent component analysis." Signal Processing 155 (February 2019): 368–76. http://dx.doi.org/10.1016/j.sigpro.2018.07.029.

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25

Stone, James V. "Independent component analysis: an introduction." Trends in Cognitive Sciences 6, no. 2 (2002): 59–64. http://dx.doi.org/10.1016/s1364-6613(00)01813-1.

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26

Oja, Erkki, Stefan Harmeling, and Luis Almeida. "Independent component analysis and beyond." Signal Processing 84, no. 2 (2004): 215–16. http://dx.doi.org/10.1016/j.sigpro.2003.11.005.

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27

Barbedor, Pascal. "Independent component analysis by wavelets." TEST 18, no. 1 (2007): 136–55. http://dx.doi.org/10.1007/s11749-007-0073-7.

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28

Ma, Libo, and Liqing Zhang. "Overcomplete topographic independent component analysis." Neurocomputing 71, no. 10-12 (2008): 2217–23. http://dx.doi.org/10.1016/j.neucom.2007.06.013.

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29

Knaak, Mirko, Shoko Araki, and Shoji Makino. "Geometrically Constrained Independent Component Analysis." IEEE Transactions on Audio, Speech and Language Processing 15, no. 2 (2007): 715–26. http://dx.doi.org/10.1109/tasl.2006.876730.

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30

Ranjith, Jayasanthi, and N. J. R. Muniraj. "High Performance Independent Component Analysis." Asian Journal of Scientific Research 7, no. 4 (2014): 460–71. http://dx.doi.org/10.3923/ajsr.2014.460.471.

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31

Suzuki, Taiji, and Masashi Sugiyama. "Least-Squares Independent Component Analysis." Neural Computation 23, no. 1 (2011): 284–301. http://dx.doi.org/10.1162/neco_a_00062.

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Accurately evaluating statistical independence among random variables is a key element of independent component analysis (ICA). In this letter, we employ a squared-loss variant of mutual information as an independence measure and give its estimation method. Our basic idea is to estimate the ratio of probability densities directly without going through density estimation, thereby avoiding the difficult task of density estimation. In this density ratio approach, a natural cross-validation procedure is available for hyperparameter selection. Thus, all tuning parameters such as the kernel width or

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32

Bonhomme, Stéphane, and Jean-Marc Robin. "Consistent noisy independent component analysis." Journal of Econometrics 149, no. 1 (2009): 12–25. http://dx.doi.org/10.1016/j.jeconom.2008.12.019.

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33

Wang, Jing Hui, and Shu Gang Tang. "Quadratic Independent Component Analysis Based on Sparse Component." Applied Mechanics and Materials 442 (October 2013): 562–67. http://dx.doi.org/10.4028/www.scientific.net/amm.442.562.

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In this paper, a novel signal blind separation using adaptive multi-resolution independent component analysis based on sparse component is presented. This method separates mixed signal based on quadratic function and sparse representation. The quadratic function can be interpreted as the time-frequency function or time-scale function, or other. The sparse expression is the original signal through the dictionary to get their coefficients. Most of the coefficients is very small, close to zero, can greatly save separate computing time. At the same time this method can filter out the noise. The ar

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34

NiketBorade, Sushma, and Ratnadeep R. Deshmukh. "Comparative Study of Principal Component Analysis and Independent Component Analysis." International Journal of Computer Applications 92, no. 15 (2014): 45–49. http://dx.doi.org/10.5120/16087-5399.

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35

Khokher, Rohit, Ram Chandra Singh, and Rahul Kumar. "Footprint Recognition with Principal Component Analysis and Independent Component Analysis." Macromolecular Symposia 347, no. 1 (2015): 16–26. http://dx.doi.org/10.1002/masy.201400045.

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36

Saju, S., and G. Thirugnanam. "Performance Analysis of Image Watermarking Using Contourlet Transform and Extraction Using Independent Component Analysis." Journal of Computers 11, no. 3 (2016): 258–65. http://dx.doi.org/10.17706/jcp.11.3.258-265.

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37

Feng, Pingxing, and Liping Li. "On extending the Noisy Independent Component Analysis to Impulsive Components." Wireless Personal Communications 88, no. 3 (2015): 415–27. http://dx.doi.org/10.1007/s11277-015-3135-2.

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38

Wang, Jing. "Mixed principal-component-analysis/independent-component-analysis transform for hyperspectral image analysis." Optical Engineering 46, no. 7 (2007): 077006. http://dx.doi.org/10.1117/1.2759225.

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39

Cheng, Wei, Zhousuo Zhang, Jie Zhang, and Jiantao Lu. "Acoustical Source Tracing Using Independent Component Analysis and Correlation Analysis." Shock and Vibration 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/571206.

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Acoustical signals from mechanical systems reveal the operating conditions of mechanical components and thus benefit for machinery condition monitoring and fault diagnosis. However, the acoustical signals directly measured by the sensors in essential are the mixed signals of all the sources, and normally it is very difficult to be used for source identification or operating feature extraction. Therefore, this paper studies the acoustical source tracing problem using independent component analysis (ICA) and identifies the sources using correlation analysis: the measured acoustical signals are s

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40

Rascon, Caleb, Barry Lennox, and Ognjen Marjanovic. "Recovering Independent Components from Shifted Data Using Fast Independent Component Analysis and Swarm Intelligence." Applied Spectroscopy 63, no. 10 (2009): 1142–51. http://dx.doi.org/10.1366/000370209789553192.

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41

Ichiki, Kiyotomo, Ryohei Kaji, Hiroaki Yamamoto, Tsutomu T. Takeuchi, and Yasuo Fukui. "CO COMPONENT ESTIMATION BASED ON THE INDEPENDENT COMPONENT ANALYSIS." Astrophysical Journal 780, no. 1 (2013): 13. http://dx.doi.org/10.1088/0004-637x/780/1/13.

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42

Seok, Jong-Won. "Audio Watermarking Using Independent Component Analysis." Journal of information and communication convergence engineering 10, no. 2 (2012): 175–80. http://dx.doi.org/10.6109/jicce.2012.10.2.175.

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43

SHU, Lang, Qin SHU, and Jing SU. "Independent component analysis with innovation model." Journal of Computer Applications 31, no. 2 (2011): 556–58. http://dx.doi.org/10.3724/sp.j.1087.2011.00556.

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44

Yokote, Ryota, and Yasuo Matsuyama. "Rapid Algorithm for Independent Component Analysis." Journal of Signal and Information Processing 03, no. 03 (2012): 275–85. http://dx.doi.org/10.4236/jsip.2012.33037.

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45

Ibrahim, Wan Nurhidayah, Mohd Syahid Anuar, Ali Selamat, and Ondrej Krejcar. "BOTNET DETECTION USING INDEPENDENT COMPONENT ANALYSIS." IIUM Engineering Journal 23, no. 1 (2022): 95–115. http://dx.doi.org/10.31436/iiumej.v23i1.1789.

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Botnet is a significant cyber threat that continues to evolve. Botmasters continue to improve the security framework strategy for botnets to go undetected. Newer botnet source code runs attack detection every second, and each attack demonstrates the difficulty and robustness of monitoring the botnet. In the conventional network botnet detection model that uses signature-analysis, the patterns of a botnet concealment strategy such as encryption & polymorphic and the shift in structure from centralized to decentralized peer-to-peer structure, generate challenges. Behavior analysis seems to b

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46

Bartlett, M. S., J. R. Movellan, and T. J. Sejnowski. "Face recognition by independent component analysis." IEEE Transactions on Neural Networks 13, no. 6 (2002): 1450–64. http://dx.doi.org/10.1109/tnn.2002.804287.

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47

Plumbley, M. D. "Algorithms for nonnegative independent component analysis." IEEE Transactions on Neural Networks 14, no. 3 (2003): 534–43. http://dx.doi.org/10.1109/tnn.2003.810616.

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48

James, Christopher J., and Christian W. Hesse. "Independent component analysis for biomedical signals." Physiological Measurement 26, no. 1 (2004): R15—R39. http://dx.doi.org/10.1088/0967-3334/26/1/r02.

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49

Xie, Xiaobo, Fatih Yaman, Xiang Zhou, and Guifang Li. "Polarization Demultiplexing by Independent Component Analysis." IEEE Photonics Technology Letters 22, no. 11 (2010): 805–7. http://dx.doi.org/10.1109/lpt.2010.2046158.

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50

Pang, X., and S. Y. Lee. "Independent component analysis for beam measurements." Journal of Applied Physics 106, no. 7 (2009): 074902. http://dx.doi.org/10.1063/1.3226858.

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