• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.383-392
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• 2000

Since late 1970, methods of influence or sensitivity analysis for detecting influential observations have been studied not only in regression and related methods but also in various multivariate methods. If results of multivariate analyses sometimes depend heavily on a small number of observations, we should be very careful to draw a conclusion. Similar phenomena may also occur in the case of incomplete data. In this research we try to study such influential observations in multivariate statistical analysis of incomplete data. Case of principal component analysis is studied with a numerical example.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.61-74
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• 2020

Interval-valued data, one type of symbolic data, are observed in the form of intervals rather than single values. Each interval-valued observation has an internal variation. Principal component analysis reduces the dimension of data by maximizing the variance of data. Therefore, the principal component analysis of the interval-valued data should account for the variance between observations as well as the variation within the observed intervals. In this paper, three principal component analysis methods for interval-valued data are summarized. In addition, a new method using a truncated normal distribution has been proposed instead of a uniform distribution in the conventional quantile method, because we believe think there is more information near the center point of the interval. Each method is compared using simulations and the relevant data set from the OECD. In the case of the quantile method, we draw a scatter plot of the principal component, and then identify the position and distribution of the quantiles by the arrow line representation method.

• Journal of the Mineralogical Society of Korea
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• v.26 no.3
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• pp.189-196
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• 2013

In this study, we tried to define the origin of fine-grained sediments in Southwestern Cheju Island Mud (SWCIM) using principal component analysis. We used relative clay mineral compositions using 138 marine surface sediments, 4 Huanghe sediments and 3 Changjiang river sediments by the semi-quantitative X-ray diffraction analysis. We made bioplot diagram using R program with principal component 1 and component 2 because they might contain more than 90% of all data. Although the distribution pattern of each clay minerals in SWCIM is so intricate, smectite and kaolinite contents are high in the west region, but illite and chlorite contents are rich in the east region. In the biplot, the east region of SWCIM distribute around Changjiang river, whereas west region of SWCIM disperse around Huanghe. Our results might reveal that west region of SWCIM is mainly originated by Huanghe, but east region of SWCIM by Changjiang River.

• Applied Biological Chemistry
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• v.47 no.3
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• pp.300-306
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• 2004

Eight cation or anion-responsive polymer membranes were prepared by a casting procedure employing polyvinyl chloride, Bis (2-ethylhexyl)sebacate and each electroactive material in the ratio of 66 : 33 : 1. The resulting membranes were separately installed onto the sensitive area of the ionic electrodes to produce an 8-channel taste sensor array. The taste sensors of the array were connected to a high-input impedance amplifier and the amplified sensor signals were interfaced to a PC via an A/D converter. The taste evaluation system was applied to a discriminant analysis on six groups of marketed beverages like sikhye, sujunggwa, tangerine juice, ume juice, ionic drink and green tea. When the signal data from the sensor array were analyzed by principal component analysis after normalization, the 1st, 2nd and 3rd principal component explained most of the total data variance. The six groups of the analyzed beverages were discriminated well in the three dimensional principal component space. The half of the five groups of the analyzed beverages was also discriminated in the two dimensional principal component plane.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.515-518
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• 2017

I used Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) to utilize biometric technology for security. I used 14 korean consonants(ㄱ to ㅎ). And It has both information of gestures for each consonants and identity of user. So this experiment is set for this two aspects. I used database including 20 people's images. Each person did 140 action for every consonant with 10 trials. PCA and LDA must be applied on self-collected database using MATLAB programming. Equal Error Rate (EER) is used for evaluate performance of this analysis.

• Journal of the Mineralogical Society of Korea
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• v.27 no.3
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• pp.107-114
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• 2014

In this study, we tried to determine the origin of fine-grained sediments in Southeastern Yellow Sea Mud patch (SEYSM) using principal component analysis coupled with semi-quantitative X-ray diffraction analysis for 4 major clay minerals. We used 51 marine surface sediments from SEYSM and 33 surface sediments of rivers flowing into the Yellow Sea. We made bioplot diagram using R program with principal component 1 and component 2 because the two components might contain about 98% of all data. The content of each clay mineral in the south and north regions of SEYSM are almost similar. In the biplot, SEYSM sediments distribute close to Korean rivers sediments than Huanghe and Changjiang sediments. Based on these results, we suggest that SEYSM is originated from the Korean rivers sediments. The higher accumulation rate in the SEYSM compared to the sediment discharge from neighboring Korean rivers can be explained by erosion and reworking of surface sediments in this area. The principal component analysis can be used for the provenance research of marine sediments around the Korean Peninsula.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.193-199
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• 2008

In this paper, it is proposed the implementation of face authentication system based on real-time image processing. We described the process implementing the two steps for real-time face authentication system. At first face detection steps, we describe the face detection by using feature of wavelet transform, LoG operator and hausdorff distance matching. In the second step we describe the new dual-line principal component analysis(PCA) for real-time face recognition. It is combines horizontal line to vertical line so as to accept local changes of PCA. The proposed system is affected a little by the video size and resolution. And then simulation results confirm the effectiveness of out system and demonstrate its superiority to other conventional algorithm. Finally, the possibility of performance evaluation and real-time processing was confirmed through the implementation of face authentication system.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.113-118
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• 1996

Principal component analysis(PCA) is an essential technique for data compression and feature extraction, and has been widely used in statistical data analysis, communication theory, pattern recognition, and image processing. Oja(1992) found that a linear neuron with constrained Hebbian learning rule can extract the principal component by using stochastic gradient ascent method. In practice real data often contain some outliers. These outliers will significantly deteriorate the performances of the PCA algorithms. In order to make PCA robust, Xu & Yuille(1995) applied statistical physics to the problem of robust principal component analysis(RPCA). Devlin et.al(1981) obtained principal components by using techniques such as M-estimation. The propose of this paper is to investigate from the statistical point of view how Xu & Yuille's(1995) RPCA works under the same simulation condition as in Devlin et.al(1981).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.295-298
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• 2007

본 논문에서는 사람의 얼굴표정을 통해 4개의 기본감정(기쁨, 슬픔, 화남, 놀람)에 대한 특징을 추출하고 인식하여 그 결과를 이용하여 감정표현 시스템을 구현한다. 먼저 주성분 분석(Principal Component Analysis)법을 이용하여 고차원의 영상 특징 데이터를 저차원 특징 데이터로 변환한 후 이를 선형 판별 분석(Linear Discriminant Analysis)법에 적용시켜 좀 더 효율적인 특징벡터를 추출한 다음 감정을 인식하고, 인식된 결과를 얼굴 표현 시스템에 적용시켜 감정을 표현한다.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.6
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• pp.590-596
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• 2004

Neural network-based signal classification systems are increasingly used in the analysis of large volumes of data obtained in NDE applications. Ultrasonic inspection methods on the other hand are commonly used in the nondestructive evaluation of welds to detect flaws. An important characteristic of ultrasonic inspection is the ability to identify the type of discontinuity that gives rise to a peculiar signal. Standard techniques rely on differences in individual A-scans to classify the signals. This paper proposes an ultrasonic signal classification technique based on the information tying in the neighboring signals. The approach is based on a 2-dimensional Fourier transform and the principal component analysis to generate a reduced dimensional feature vector for classification. Results of applying the technique to data obtained from the inspection of actual steel welds are presented.