• 응용통계연구
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• 제8권1호
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• pp.51-60
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• 1995

크기가 $n \times p$인 자료행렬에서 p개의 변수들과 성격이 다소 다른 $p_s$개의 변수를 같이 고려한 크기가 $n \times (p + p_s)$ 자료행렬이 있다 하자. 전통적 주성성분분석은 성격이 다른 변수들로 인하여 효과적인 결과를 제공하지 못한다. 본 논문에서는 이런 점을 개선하기 위해서 성격이 다른 $p_s$개의 변수를 추가변수로 두는 추가적 주성분분석을 소개하려 한다. 이 기법은 전통적 주성분분석의 대수적,기하적인 면을 따른다. 그리고 전통적 주성분분석과 추가적 주성성분분석을 활용한 한국 프로야구의 8개팀과 1982-1992년 동안의 14개의 부문별 기록에 대한 전형적인 자료분석의 한 예를 제시한다. 더불어 두 분석의 결과도 비교하였다.

• 농업과학연구
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• 제47권1호
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• pp.67-81
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• 2020

The physicochemical parameters of honey are used to determine the botanic origin of honey and to specify the composition criteria for honey in regulations and standards. The parameters of honeydew and blossom honeys from Korean beekeepers were determined to investigate whether they complied with the composition criteria for honey in the food code legislated by Korean authority and to establish the parameters which should be subjected to principal component analysis for improved differentiation of honeys. The fructose and glucose contents of the honeydew honey did not comply with the composition criteria. The ash content of the honey was closely correlated with CIE a^* and CIE L^* The principal component analysis of fructose to glucose ratio, CIE a^*, CIE L^*, ash content, free acidity, and fructose and glucose contents enabled classification of honeydew, chestnut, multifloral, and acacia honeys. Additional advantage of the principal component analysis (PCA) is that the physicochemical parameters, such as fructose to glucose ratio (F/G) and color, can be determined using the analytical instruments for composition criteria and quality control of honey. This study suggested that composition criteria for honeydew honey should be established in the food code in accordance with international standards. The principal component analysis reported in this study resulted in improved classification of the honeys from Korean beekeepers.

• 대한수학회지
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• 제57권2호
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• pp.401-414
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• 2020

It is computationally expensive to compute principal components from scratch at every update or downdate when new data arrive and existing data are truncated from the data matrix frequently. To overcome this limitations, incremental principal component analysis is considered. Specifically, we present a sliding window based efficient incremental principal component computation from a covariance matrix which comprises of two procedures; simultaneous update and downdate of principal components, followed by the rank-one matrix update. Additionally we track the accurate decomposition error and the adaptive numerical rank. Experiments show that the proposed algorithm enables a faster execution speed and no-meaningful decomposition error differences compared to typical incremental principal component analysis algorithms, thereby maintaining a good approximation for the principal components.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.367-379
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• 2012

야구경기에서 순위를 예측하는 것은 야구팬들에게 관심의 대상이 된다. 이러한 순위를 예측하기 위해서 2011년 한국프로야구 기록 자료를 바탕으로 산술평균방법, 가중평균방법, 주성분분석방법, 주성분회귀분석 방법을 제시한다. 표준화를 통한 산술평균, 상관계수를 이용한 가중평균과 주성분 분석을 이용해서 순위를 예측하고, 최종모형으로 주성분회귀분석 모형이 선택되었다. 주성분 분석으로 축약된 변수를 이용해서 회귀분석을 실시하여, 투수부분, 타자부분, 투수와 타자부분의 순위예측 모형을 제안한다. 예측된 회귀모형을 통해서 2012년도 순위 예측이 가능하다.

• 응용통계연구
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• 제30권1호
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• pp.135-145
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• 2017

주성분 분석(principal component analysis; PCA)은 서로 상관되어 있는 다변량 자료의 차원을 축소하는 대표적인 기법으로 많은 다변량 분석에서 활용되고 있다. 하지만 주성분은 모든 변수들의 선형결합으로 이루어지므로, 그 결과의 해석이 어렵다는 한계가 있다. sparse PCA(SPCA) 방법은 elastic net 형태의 벌점함수를 이용하여 보다 성긴(sparse) 적재를 가진 수정된 주성분을 만들어주지만, 변수들의 그룹구조를 이용하지 못한다는 한계가 있다. 이에 본 연구에서는 기존 SPCA를 개선하여, 자료가 그룹화되어 있는 경우에 유의한 그룹을 선택함과 동시에 그룹 내 불필요한 변수를 제거할 수 있는 새로운 주성분 분석 방법을 제시하고자 한다. 그룹과 그룹 내 변수 구조를 모형 적합에 이용하기 위하여, sparse 주성분 분석에서의 elastic net 벌점함수 대신에 계층적 벌점함수 형태를 고려하였다. 또한 실제 자료의 분석을 통해 제안 방법의 성능 및 유용성을 입증하였다.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.1057-1068
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• 2003

Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.

• 한국의류학회지
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• 제14권3호
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• pp.208-215
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• 1990

The purpose of this study is to provide the fundamental data of a dummy design for more suitable ready made clothing by making a pattern of somatic types and analyzing their morphological characteristics in accordance with different pattern of somatic types. The side view silhouettes of 90 junior high school girls of age $13\~16$ in seoul urban area were measured by means of the plan photographing and the low data were examined by principal component analysis, while the principal component analysis was applied and three components were extracted and then interpreted to explain to variation of the form of the body. Using three components respectively the cluster analysis was carried out and the subject classified into 4 cluster The following outcomes are obtained. . The results of principal component analysis of this study would be turned out the three; 1) The first principal component shows the degree of erectness or stoop of the figure. 2) The second principal component was a stature length or a growth rate. 3) The third principal component was the obesity component. 2. The results of cluster analysis by using three principal component analysis would be turned out the four cluser; 1) Cluster 1 ($29\%$ of the total) is characterized with lower stature. 2) Cluster 2 ($21\%$ of the total) is characterized with backward somatotype, and the highest leg. 3) Cluster 3 ($23\%$ of the total) is thicked back of neck. 4) Cluster 4 ($27\%$ of the total) is characterized with forward somatotype, and highest stature, height.

• 대한인간공학회지
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• 제10권2호
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• pp.31-42
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• 1991

To understand the shape of the neck in a view of garment planning, principal component analysis has been appliedto the measurement of the neck. The neck surface development and the cross sections of the neck have been observed. The materials consist of the body mearsurements, the neck surface developments and the cross sec- tions of the necks of a total of 108 korean woman students. The difference between the right side and the left side of the neck has not been reconginiged. But the differenece among the height of the front neck point, that of the side neck point and that of the back neck point has been recognized. 2. The initial 41 items have been found having variety and duplication. So two criteria have been made to solve those problems and the selection of 34 items have been made by each criterion. 3. 43 and 34 items have been compared by means of accumulative ratios of contribution and of clearness within the meaning of principal component. As a result, 34 measurement items have been further anylysis. 4. As a result of principal component analysis on the 34 items, the four principal components have been found obtaines and inter-preted. The four principal components are 1) the thick of the neck, 2) the front neck-line on the waist basic pattern, basic pattern, 3) the shape of the neck surface development, and 4) the back neck-line on the waist basic pattern. 5. According to the graphic informations concerning these principal components, the meaning of these four principal components has been grasped on the visual. As a result, there is a large individual difference in the shape of neck.

• 한국농공학회논문집
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• 제55권1호
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• pp.31-38
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• 2013

The purpose of this study was to evaluate climate change vulnerability over the agricultural infrastructure in terms of flood and drought using principal component analysis. Vulnerability was assessed using vulnerability resilience index (VRI) which combines climate exposure, sensitivity, and adaptive capacity. Ten flood proxy variables and six drought proxy variables for the vulnerability assessment were selected by opinions of researchers and experts. The statistical data on 16 proxy variables for the local governments (Si, Do) were collected. To identify major variables and to explain the trend in whole data set, principal component analysis (PCA) was conducted. The result of PCA showed that the first 3 principal components explained approximately 83 % and 89 % of the total variance for the flood and drought, respectively. VRI assessment for the local governments based on the PCA results indicated that provinces where having the relatively large cultivation areas were categorized as vulnerable to climate change.

• 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.