• Title/Summary/Keyword: canonical correlation analysis

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Higher-order solutions for generalized canonical correlation analysis

  • Kang, Hyuncheol
    • Communications for Statistical Applications and Methods
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    • v.26 no.3
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    • pp.305-313
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    • 2019
  • Generalized canonical correlation analysis (GCCA) extends the canonical correlation analysis (CCA) to the case of more than two sets of variables and there have been many studies on how two-set canonical solutions can be generalized. In this paper, we derive certain stationary equations which can lead the higher-order solutions of several GCCA methods and suggest a type of iterative procedure to obtain the canonical coefficients. In addition, with some numerical examples we present the methods for graphical display, which are useful to interpret the GCCA results obtained.

Nonlinear Canonical Correlation Analysis for Paralysis Disease Data

  • Shin, Yang-Kyu
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.3
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    • pp.515-521
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    • 2004
  • Categorical data are mostly found in oriental medical research. The nonlinear canonical correlation analysis does not assume an interval level of measurement. In this paper, we apply nonlinear canonical correlation analysis to quantification and explain how similar sets of variables are to one another for paralysis disease data.

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Multivariate Volatility Analysis via Canonical Correlations for Financial Time Series (정준상관분석을 통한 다변량 금융시계열의 변동성 분석)

  • Lee, Seung Yeon;Hwang, S.Y.
    • The Korean Journal of Applied Statistics
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    • v.27 no.7
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    • pp.1139-1149
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    • 2014
  • Multivariate volatility is summarized through canonical correlation analysis (CCA). Along with the standard CCA, non-negative and sparse canonical correlation analysis (NSCCA) is introduced to make sure that volatility coefficients are non-negative and the number of coefficients in the volatility CCA is as small as possible. Various multivariate financial time series are analyzed to illustrate the main contribution of the paper.

Underwater Target Analysis Using Canonical Correlation Analysis (정준상관분석을 이용한 수중표적 분석)

  • Seok, Jong-Won;Kim, Tae-Hwan;Bae, Keun-Sung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.9
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    • pp.1878-1883
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    • 2012
  • Generally, in the underwater target recognition, feature vectors are extracted from the target signal utilizing spatial information according to target shape/material characteristics. And, various signal processing techniques have been studied to extract feature vectors which is less sensitive to the location of the receiver. In this paper, we analyzed the characteristics of synthesized underwater objects using canonical correlation analysis method which is relatively less sensitive to the location of receiver. Canonical correlation analysis is applied to two consecutive backscattered sonar returns at different aspect angles to analyze the correlation characteristics in multi-aspect environment.

Semi-Partial Canonical Correlation Biplot

  • Lee, Bo-Hui;Choi, Yong-Seok;Shin, Sang-Min
    • The Korean Journal of Applied Statistics
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    • v.25 no.3
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    • pp.521-529
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    • 2012
  • Simple canonical correlation biplot is a graphical method to investigate two sets of variables and observations in simple canonical correlation analysis. If we consider the set of covariate variables that linearly affects two sets of variables, we can apply the partial canonical correlation biplot in partial canonical correlation analysis that removes the linear effect of the set of covariate variables on two sets of variables. On the other hand, we consider the set of covariate variables that linearly affect one set of variables but not the other. In this case, if we apply the simple or partial canonical correlation biplot, we cannot clearly interpret other two sets of variables. Therefore, in this study, we will apply the semi-partial canonical correlation analysis of Timm (2002) and remove the linear effect of the set of covariate variables on one set of variables but not the other. And we suggest the semi-partial canonical correlation biplot for interpreting the semi-partial canonical correlation analysis. In addition, we will compare shapes and shape the variabilities of the simple, partial and semi-partial canonical correlation biplots using a procrustes analysis.

An Application of Canonical Correlation Analysis Technique to Land Cover Classification of LANDSAT Images

  • Lee, Jong-Hun;Park, Min-Ho;Kim, Yong-Il
    • ETRI Journal
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    • v.21 no.4
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    • pp.41-51
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    • 1999
  • This research is an attempt to obtain more accurate land cover information from LANDSAT images. Canonical correlation analysis, which has not been widely used in the image classification community, was applied to the classification of a LANDSAT images. It was found that it is easy to select training areas on the classification using canonical correlation analysis in comparison with the maximum likelihood classifier of $ERDAS^{(R)}$ software. In other words, the selected positions of training areas hardly affect the classification results using canonical correlation analysis. when the same training areas are used, the mapping accuracy of the canonical correlation classification results compared with the ground truth data is not lower than that of the maximum likelihood classifier. The kappa analysis for the canonical correlation classifier and the maximum likelihood classifier showed that the two methods are alike in classification accuracy. However, the canonical correlation classifier has better points than the maximum likelihood classifier in classification characteristics. Therefore, the classification using canonical correlation analysis applied in this research is effective for the extraction of land cover information from LANDSAT images and will be able to be put to practical use.

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Canonical Correlation: Permutation Tests and Regression

  • Yoo, Jae-Keun;Kim, Hee-Youn;Um, Hye-Yeon
    • Communications for Statistical Applications and Methods
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    • v.19 no.3
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    • pp.471-478
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    • 2012
  • In this paper, we present a permutation test to select the number of pairs of canonical variates in canonical correlation analysis. The existing chi-squared test is known to be limited to normality in use. We compare the existing test with the proposed permutation test and study their asymptotic behaviors through numerical studies. In addition, we connect canonical correlation analysis to regression and we we show that certain inferences in regression can be done through canonical correlation analysis. A regression analysis of real data through canonical correlation analysis is illustrated.

UNIFYING STATIONARY EQUATIONS FOR GENERALIZED CANONICAL CORRELATION ANALYSIS

  • Kang Hyun-Cheol;Kim Kee-Young
    • Journal of the Korean Statistical Society
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    • v.35 no.2
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    • pp.143-156
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    • 2006
  • In the present paper, various solutions for generalized canonical correlation analysis (GCCA) are considered depending on the criteria and constraints. For the comparisons of some characteristics of the solutions, we provide with certain unifying stationary equations which might to also useful to obtain various generalized canonical correlation analysis solutions. In addition, we suggest an approach for the generalized canonical correlation analysis by exploiting the concept of maximum eccentricity originally de-signed to test the internal independence structure. The solutions, including new one, are compared through unifying stationary equations and by using some numerical illustrations. A type of iterative procedure for the GCCA solutions is suggested and some numerical examples are provided to illustrate several GCCA methods.

Microphone Type Classification for Digital Audio Forgery Detection (디지털 오디오 위조검출을 위한 마이크로폰 타입 인식)

  • Seok, Jongwon
    • Journal of Korea Multimedia Society
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    • v.18 no.3
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    • pp.323-329
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    • 2015
  • In this paper we applied pattern recognition approach to detect audio forgery. Classification of the microphone types and models can help determining the authenticity of the recordings. Canonical correlation analysis was applied to extract feature for microphone classification. We utilized the linear dependence between two near-silence regions. To utilize the advantage of multi-feature based canonical correlation analysis, we selected three commonly used features to capture the temporal and spectral characteristics. Using three different microphones, we tested the usefulness of multi-feature based characteristics of canonical correlation analysis and compared the results with single feature based method. The performance of classification rate was carried out using the backpropagation neural network. Experimental results show the promise of canonical correlation features for microphone classification.

Partial Canonical Correlation Biplot (편정준상관 행렬도)

  • Yeom, Ah-Rim;Choi, Yong-Seok
    • The Korean Journal of Applied Statistics
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    • v.24 no.3
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    • pp.559-566
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    • 2011
  • Biplot is a useful graphical method to explore simultaneously rows and columns of two-way data matrix. In particular, canonical correlation biplot is a method for investigating two sets of variables and observations in canonical correlation analysis graphically. For more than three sets of variables, we can apply the generalized canonical correlation biplot in generalized canonical correlation analysis which is an expansion of the canonical correlation analysis. On the other hand, we consider the set of covariate variables which is affecting the linearly two sets of variables. In this case, if we apply the generalized canonical correlation biplot, we cannot clearly interpret the other two sets of variables due to the effect of the set of covariate variables. Therefor, in this paper, we will apply the partial canonical correlation analysis of Rao (1969) removing the linear effect of the set of covariate variables on two sets of variables. We will suggest the partial canonical correlation biplot for inpreting the partial canonical correlation analysis graphically.