• Title/Summary/Keyword: Principal components analysis

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Regional Geological Mapping by Principal Component Analysis of the Landsat TM Data in a Heavily Vegetated Area (식생이 무성한 지역에서의 Principal Component Analysis 에 의한 Landsat TM 자료의 광역지질도 작성)

  • 朴鍾南;徐延熙
    • Korean Journal of Remote Sensing
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    • v.4 no.1
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    • pp.49-60
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    • 1988
  • Principal Component Analysis (PCA) was applied for regional geological mapping to a multivariate data set of the Landsat TM data in the heavily vegetated and topographically rugged Chungju area. The multivariate data set selection was made by statistical analysis based on the magnitude of regression of squares in multiple regression, and it includes R1/2/R3/4, R2/3, R5/7/R4/3, R1/2, R3/4. R4/3. AND R4/5. As a result of application of PCA, some of later principal components (in this study PC 3 and PC 5) are geologically more significant than earlier major components, PC 1 and PC 2 herein. The earlier two major components which comprise 96% of the total information of the data set, mainly represent reflectance of vegetation and topographic effects, while though the rest represent 3% of the total information which statistically indicates the information unstable, geological significance of PC3 and PC5 in the study implies that application of the technique in more favorable areas should lead to much better results.

Comparison of head-related transfer function models based on principal components analysis (주성분 분석법을 이용한 머리전달함수 모형화 기법의 성능 비교)

  • Hwang, Sung-Mok;Park, Young-Jin;Park, Youn-Sik
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2008.04a
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    • pp.920-927
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    • 2008
  • This study deals with modeling of Head-Related Transfer Functions (HRTFs) using Principal Components Analysis (PCA) in the time and frequency domains. Four PCA models based on Head-Related Impulse Responses (HRIRs), complex-valued HRTFs, augmented HRTFs, and log-magnitudes of HRTFs are investigated. The objective of this study is to compare modeling performances of the PCA models in the least-squares sense and to show the theoretical relationship between the PCA models. In terms of the number of principal components needed for modeling, the PCA model based on HRIR or augmented HRTFs showed more efficient modeling performance than the PCA model based on complex-valued HRTFs. The PCA model based on HRIRs in the time domain and that based on augmented HRTFs in the frequency domain are shown to be theoretically equivalent. Modeling performance of the PCA model based on log-magnitudes of HRTFs cannot be compared with that of other PCA models because the PCA model deals with log-scaled magnitude components only, whereas the other PCA models consider both magnitude and phase components in linear scale.

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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.

Arrow Diagrams for Kernel Principal Component Analysis

  • Huh, Myung-Hoe
    • Communications for Statistical Applications and Methods
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    • v.20 no.3
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    • pp.175-184
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    • 2013
  • Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.

통계분석을 이용한 소규모 유역내 하천수 수질과 지질과의 상관관계 해석

  • 고경석;김재곤;이진수;김용제;조춘희
    • Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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    • 2004.04a
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    • pp.311-314
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    • 2004
  • To identify the effect of geology and land use, the hydrogeochemical and multivariate statitstical analysis were executed for stream water collected in headwater region of Daecheong reservoir. Hydrogeochemical analysis was showed the effect of weathering process such as dissolution of calc-silicate minerals to hydrochemistry of stream water with contrasting geology. Cluster and principal components analysis can also help to identify the source of dissolved components in stream water.

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The Study of Korean Manufacturing Industry Wage : Principal Components Regression Analysis (한국 제조업의 임금결정에 대한 연구 : 외환위기 전·후를 중심으로)

  • Oh, Yu-Jin;Park, Sung-Joon;Kim, Yu-Seop
    • Journal of Labour Economics
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    • v.28 no.1
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    • pp.61-82
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    • 2005
  • We investigate wage differentials in Korea in the manufacturing industry, as well as factors affecting structural change in wage determination for the pre- and post-financial crisis regimes. We use the 1995 and 1999 data from the Survey Report on the Wage Structure (SRWS) from the Ministry of Labor. Principal components regression analysis is used to tackle multicollinearity. We employ factor analysis to reduce a set of variables to a smaller number, which contain observed and latent variables. Our empirical investigation provide evidences for changes in wages structure between 1995 and 1999. In 1995, the job quality factor is the most critical in the determination of wages, while in 1999, the industry attributes factor impacts greatly on the wages.

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Assessment and Classification of Korean Local Corn Lines by the Application of Principal Component Analysis (I) (Principal Component Analysis Method에 의(依)한 한국재래종(韓國在來種) 옥수수의 해석(解析) 및 계통분류(系統分類)(I))

  • Lee, In Sup;Choe, Bong Ho
    • Korean Journal of Agricultural Science
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    • v.8 no.2
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    • pp.139-151
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    • 1981
  • To obtain breeding materials 57 Korean local corn lines collected were assessed and classified by the application of principal component analysis. The results obtained were as follows. 1. In the result of principal component analysis for 27 characters, 67.1% and 88.6% of total variation could be appreciated by the first four and fir st ten principal component respectively. 2. According to the value of characters and principal components, contribution of characters to principal components were very variable. 3. Biological meaning of the principal component and plant type corresponded to the each principal component were explained clear by the correlation coefficients between principal components and characters. 4. 57 lines were classified into 4 lineal groups by the taxonomic distances.

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Asymptotic Test for Dimensionality in Probabilistic Principal Component Analysis with Missing Values

  • Park, Chong-sun
    • Communications for Statistical Applications and Methods
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    • v.11 no.1
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    • pp.49-58
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    • 2004
  • In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

Comparison of Significant Term Extraction Based on the Number of Selected Principal Components (주성분 보유수에 따른 중요 용어 추출의 비교)

  • Lee Chang-Beom;Ock Cheol-Young;Park Hyuk-Ro
    • The KIPS Transactions:PartB
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    • v.13B no.3 s.106
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    • pp.329-336
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    • 2006
  • In this paper, we propose a method of significant term extraction within a document. The technique used is Principal Component Analysis(PCA) which is one of the multivariate analysis methods. PCA can sufficiently use term-term relationships within a document by term-term correlations. We use a correlation matrix instead of a covariance matrix between terms for performing PCA. We also try to find out thresholds of both the number of components to be selected and correlation coefficients between selected components and terms. The experimental results on 283 Korean newspaper articles show that the condition of the first six components with correlation coefficients of |0.4| is the best for extracting sentence based on the significant selected terms.

County-Based Vulnerability Evaluation to Agricultural Drought Using Principal Component Analysis - The case of Gyeonggi-do - (주성분 분석법을 이용한 시군단위별 농업가뭄에 대한 취약성 분석에 관한 연구 - 경기도를 중심으로 -)

  • Jang, Min-Won
    • Journal of Korean Society of Rural Planning
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    • v.12 no.1 s.30
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    • pp.37-48
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    • 2006
  • The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.