• Title, Summary, Keyword: Correlation analysis

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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.

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.

Correlation Analysis for Correlation Dimesion of EEG and Cold-heat Score (뇌파의 상관차원과 한열설문지와의 상관분석)

  • Bas, No-Soo;Park, Young-Jae;Oh, Hwan-Sup;Park, Young-Bae
    • The Journal of the Korean Medicine Diagnostics
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    • v.11 no.2
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    • pp.116-127
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    • 2007
  • Background and Purpose: Acording to chaos theory, irregular signals of electroencephalogram can interpretated by nonlinear method. Chaotic nonlinear dynamics in EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze EEG by correlation dimension and do Correlation Analysis of correlation dimension and cold-heat score Method: EEG raw data were measured during 15 minutes and choosed 40 seconds. We calculated correlation dimension and used surrogate data method for checking nonlinear data. After then do correlation analysis Result and Conclusion: Correlation dimension of channel 7 and channel 8 are showed significant correlation with cold score.

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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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A New Estimation Model for Wireless Sensor Networks Based on the Spatial-Temporal Correlation Analysis

  • Ren, Xiaojun;Sug, HyonTai;Lee, HoonJae
    • Journal of information and communication convergence engineering
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    • v.13 no.2
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    • pp.105-112
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    • 2015
  • The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

A Study on the Analysis of Correlation Decay Distance(CoDecDist) Model for Enhancing Spatial Prediction Outputs of Spatially Distributed Wind Farms (풍력발전출력의 공간예측 향상을 위한 상관관계감소거리(CoDecDist) 모형 분석에 관한 연구)

  • Hur, Jin
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.29 no.7
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    • pp.80-86
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    • 2015
  • As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is needed to estimate power outputs of wind generation resources. As a result, geographic information such as latitude and longitude plays a key role to estimate power outputs of spatially distributed wind farms. In this paper, we introduce spatial correlation analysis to estimate the power outputs produced by wind farms that are geographically distributed. We present spatial correlation analysis of empirical power output data for the JEJU Island and ERCOT ISO (Texas) wind farms and propose the Correlation Decay Distance (CoDecDist) model based on geographic correlation analysis to enhance the estimation of wind power outputs.

Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

  • Jung, Young-Mee
    • Bulletin of the Korean Chemical Society
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    • v.24 no.9
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    • pp.1345-1350
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    • 2003
  • Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra.

Evaluation of Panel Performance by Analysis of Variance, Correlation Analysis and Principal Component Analysis (패널요원 수행능력 평가에 사용된 분산분석, 상관분석, 주성분분석 결과의 비교)

  • Kim, Sang-Sook;Hong, Sung-Hie;Min, Bong-Kee;Shin, Myung-Gon
    • Korean Journal of Food Science and Technology
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    • v.26 no.1
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    • pp.57-61
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    • 1994
  • Performance of panelists trained for cooked rice quality was evaluated using analysis of variance, correlation analysis, and principal component analysis. Each method offered different information. Results showed that panleists with high F ratios (p=0.05) did not always have high correlation coefficient (p=0.05) with mean values pooled from whole panel. The results of analysis of variance for the panelists whose performance were extremely good or extremely poor were consistent with those of correlation analysis. Outliers designated by principal component analysis were different from the panelists whose performance was defined as extremely good or extremely poor by analysis of variance and correlation analysis. The results of principal component analysis descriminated the panelists with different scoring range more than different scoring trends depending on the treatments. Our study suggested combination of analysis of variance and correlation analysis provided valid basis for screening panelists.

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Measure Correlation Analysis of Network Flow Based On Symmetric Uncertainty

  • Dong, Shi;Ding, Wei;Chen, Liang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.6
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    • pp.1649-1667
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    • 2012
  • In order to improve the accuracy and universality of the flow metric correlation analysis, this paper firstly analyzes the characteristics of Internet flow metrics as random variables, points out the disadvantages of Pearson Correlation Coefficient which is used to measure the correlation between two flow metrics by current researches. Then a method based on Symmetrical Uncertainty is proposed to measure the correlation between two flow metrics, and is extended to measure the correlation among multi-variables. Meanwhile, the simulation and polynomial fitting method are used to reveal the threshold value between different correlation degrees for SU method. The statistical analysis results on the common flow metrics using several traces show that Symmetrical Uncertainty can not only represent the correct aspects of Pearson Correlation Coefficient, but also make up for its shortcomings, thus achieve the purpose of measuring flow metric correlation quantitatively and accurately. On the other hand, reveal the actual relationship among fourteen common flow metrics.

CMP cross-correlation analysis of multi-channel surface-wave data

  • Hayashi Koichi;Suzuki Haruhiko
    • Geophysics and Geophysical Exploration
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    • v.7 no.1
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    • pp.7-13
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    • 2004
  • In this paper, we demonstrate that Common Mid-Point (CMP) cross-correlation gathers of multi-channel and multi-shot surface waves give accurate phase-velocity curves, and enable us to reconstruct two-dimensional (2D) velocity structures with high resolution. Data acquisition for CMP cross-correlation analysis is similar to acquisition for a 2D seismic reflection survey. Data processing seems similar to Common Depth-Point (CDP) analysis of 2D seismic reflection survey data, but differs in that the cross-correlation of the original waveform is calculated before making CMP gathers. Data processing in CMP cross-correlation analysis consists of the following four steps: First, cross-correlations are calculated for every pair of traces in each shot gather. Second, correlation traces having a common mid-point are gathered, and those traces that have equal spacing are stacked in the time domain. The resultant cross-correlation gathers resemble shot gathers and are referred to as CMP cross-correlation gathers. Third, a multi-channel analysis is applied to the CMP cross-correlation gathers for calculating phase velocities of surface waves. Finally, a 2D S-wave velocity profile is reconstructed through non-linear least squares inversion. Analyses of waveform data from numerical modelling and field observations indicate that the new method could greatly improve the accuracy and resolution of subsurface S-velocity structure, compared with conventional surface-wave methods.