• 대한원격탐사학회지
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• 제4권1호
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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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.

• Bulletin of the Korean Chemical Society
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• 제24권9호
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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.

• Communications for Statistical Applications and Methods
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• 제20권3호
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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.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2004년도 총회 및 춘계학술발표회
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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.

• 노동경제논집
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• 제28권1호
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• pp.61-82
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• 2005

본 연구는 경제 위기 이후 한국 제조업의 산업간 임금격차가 경제위기 이전 그대로 유지되고 있는가의 여부와, 임금을 결정하는 메커니즘이 외환위기를 지나면서 어떻게 변화하였는가에 관하여 분석하였다. 분석에 사용된 자료는 1995년도와 1999년도 노동부의 "임금구조기본통계조사"이고, 주된 계량 기법으로는 요인분석(factor analysis)과 주성분 회귀분석(principal components regression)을 사용하였다. 분석 결과, 제조업의 산업간 임금격차는 더 벌어졌으며 임금결정의 주된 요인도 외환위기 이전에는 개인의 업무능력이었으나 위기 이후에는 사업체의 특성인 것으로 밝혀졌다.

• 농업과학연구
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• 제8권2호
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• pp.139-151
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• 1981

육종재료(育種材料)를 얻기 위해 수집(蒐集)된 한국(韓國) 재래종(在來種) 옥수수 57계통(系統)에 대(對)하여 주성분(主成分) 분석(分析)을 적용(適用)하여 재래종(在來種) 옥수수르 해석(解釋)하고 계통분류(系統分類)를 하였던 바 다음과 같은 결과(結果)를 얻었다. 1. 27개(個) 형질(形質)을 이용(利用)하여 실시(實施)한 주성분(主成分) 분석(分析)에서 제(第)4 주성분(主成分)까지를 가지고 전변동(全變動)의 67.09% 설명(說明)할 수 있었고, 제(第)1주성분(主成分)까지를 취(取)하면 전변동(全變動)의 88.63%를 설명(說明)할 수 있었다. 2. 형질(形質)의 주성분(主成分)에 대(對)한 기여율(寄與率)은 형질(形質)과 주성분(主成分)에 따라 큰 차이(差異)가 있었다. 3. 주성분(主成分)과 형질문(形質問)에 상관계수(相關係數)는 주성분(主成分)의 생물학적(生物學的) 의의(意義)와 주성분(主成分)에 대응(對應)한 식물체의 type을 명확(明確)히 하였다. 4. 계통간거리(系統間距離)에 의(依)해 57계통(系統)을 4개(個)의 계통군(系統群)으로 분류(分類)하였다.

• Communications for Statistical Applications and Methods
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• 제11권1호
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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.

• 정보처리학회논문지B
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• 제13B권3호
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• pp.329-336
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• 2006

문서를 구성하는 단어들은 서로 연관이 있다는 정보를 충분히 이용할 수 있는 다변량 분석 방법 중, 주성분분석(Principal Component Analysis)을 이용하여 중요 용어를 추출하고자 한다. 본 논문에서는 주성분분석의 분석 대상을 용어 사이의 공분산행렬이 아닌 상관행렬을 이용한다. 그리고, 중요 용어를 추출하기 위해서, 보유해야 할 주성분 개수와 주성분과 용어 사이의 상관계수에 대한 최적의 임계치를 찾고자 한다. 283건의 신문기사를 대상으로, 추출된 용어에 기반한 문장 추출 실험 결과, 첫 6개까지의 주성분과 상관계수 |0.4|라는 조건에서 가장 좋은 성능을 보였다.

• 농촌계획
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• 제12권1호
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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.