• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.813-822
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• 2010

We investigate a possible achievement in dimension reduction of time series via partially quantified principal component. Partial quantification technique allows us in modeling to accommodate artificial variable(s) of practical importance which is defined subjectively by the data analyst. Suggested procedures are described and in turn illustrated in detail by analyzing monthly unemployment rates in Korea.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.615-627
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• 2022

Principal Fitted Component (PFC) is a semi-parametric sufficient dimension reduction (SDR) method, which is originally proposed in Cook (2007). According to Cook (2007), the PFC has a connection with other usual non-parametric SDR methods. The connection is limited to sliced inverse regression (Li, 1991) and ordinary least squares. Since there is no direct comparison between the two approaches in various forward regressions up to date, a practical guidance between the two approaches is necessary for usual statistical practitioners. To fill this practical necessity, in this paper, we newly derive a connection of the PFC to covariance methods (Yin and Cook, 2002), which is one of the most popular SDR methods. Also, intensive numerical studies have done closely to examine and compare the estimation performances of the semi- and non-parametric SDR methods for various forward regressions. The founding from the numerical studies are confirmed in a real data example.

• 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.

• Journal of Plant Biology
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• v.15 no.1
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• pp.33-41
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• 1972

Nine shrub layer communities under two relatively well conserved natural summer green forests in the central region of Korean Peninsula were studied for the pattern of stem distribution in terms of Greig-Smith's multiple split-plot experiment and for the association between the population of the two main species in terms of Kershaw's covariance analysis respectively. Four contiguous belt transects, $4{\times}64m size with 1{\times}1m$ basic unit, were set in each shrub layer communities. Significant primary clumps with $1{\times}1m or 1{\times}2m$ dimension wer observed consistently throughout the nine study sites. The primary clumps themselves were significantly distributed either regularly or at random. The association between the two principal species of each shrub layer is highly significantly either positive or negative in $1{\times}1m or 1{\times}2m$ dimension. As the plot size increases from $1{\times}1m to 8{\times}8m$ the associational trends were changed from negative to positive direction in one forests. But the change from positive to negative direction and the consistent negative association were also observed from the other forest. All of the association trends were observed only from $1{\times}1m to 4{\times}4m$ dimension. These results are suggestive that the distributional pattern of the shrub layer species under the summer green forest is simple mosaic fashioned with $1{\times}1m or 1{\times}2m$ dimension. The rest of the principal species are located in that matrix. The simple mosaic pattern of two principal species are located in that matrix. The simple mosaic pattern of two principal species seems to be controlled by change in micro-environmental pattern. Differences between the primary random group and clumped group among sites also suggest that competition exists for light or/and soil between primary clumped groups.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.594-600
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• 2017

The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.

• Bulletin of the Korean Mathematical Society
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• v.36 no.4
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• pp.655-660
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• 1999

Let R be an integral domain with identity. In this paper we will show that if R is integrally closed or if t-dim $R{\leq}1$, then R[{$X_{\alpha}$}] satisfies the principal ideal theorem for each family {$X_{\alpha}$} of algebraically independent indeterminates if and only if R is an S-domain and it satisfies the principal ideal theorem.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.69-72
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• 2003

The paper considers partial least squares (PLS) as a new dimension reduction technique for the feature vector to overcome the small sample size problem in face recognition. Principal component analysis (PCA), a conventional dimension reduction method, selects the components with maximum variability, irrespective of the class information. So PCA does not necessarily extract features that are important for the discrimination of classes. PLS, on the other hand, constructs the components so that the correlation between the class variable and themselves is maximized. Therefore PLS components are more predictive than PCA components in classification. The experimental results on Manchester and ORL databases show that PLS is to be preferred over PCA when classification is the goal and dimension reduction is needed.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.492-499
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• 2022

This study experimentally investigates the effect of dimensionality reduction of vibration signal on fault diagnosis of a marine engine. By using the principal component analysis, a vibration signal having the dimension of 513 is converted into a low-dimensional signal having the dimension of 1 to 15, and the variation in fault diagnosis accuracy according to the dimensionality change is observed. The vibration signal measured from a full-scale marine generator diesel engine is used, and the contribution of the dimension-reduced signal is quantitatively evaluated using two kinds of variable importance analysis algorithms which are the integrated gradients and the feature permutation methods. As a result of experimental data analysis, the accuracy of the fault diagnosis is shown to improve as the number of dimensions used increases, and when the dimension approaches 10, near-perfect fault classification accuracy is achieved. This shows that the dimension of the vibration signal can be considerably reduced without degrading fault diagnosis accuracy. In the variable importance analysis, the dimension-reduced principal components show higher contribution than the conventional statistical features, which supports the effectiveness of the dimension-reduced signals on fault diagnosis.

• Journal of the Korean Home Economics Association
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• v.26 no.3
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• pp.1-12
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• 1988

The objectives of the study were two folds. The first objective was to determine the dimensions of the evaluative criteria of various clothing items (underwear, pajamas, jeans, blouse, two-piece, coat). The second objective was to compare the importance of the dimensions according to the clothing items and the socioeconomic status of the subjects. The questionnaires were administered to college female students living in Seoul. Principal component factor analysis with varimax rotation and ANOVA were used for the analysis. The results were as follows; 1) The evaluative criteria dimensions were found to be different according to clothing items. (1) In underwear, pajamas, jeans, evaluative criteria were classified into Aesthetic dimension, economic dimension and Functional dimension. (2) In blouse, two-piece, coat, evaluative criteria were classified into Aesthetic dimension and practical dimension. 2) there were partially significant differences in placing importance on each evaluative criteria dimension between socio-economic groups. (1) In jeans, there was a significant difference in placing importance on Aesthetic dimension between socioeconomic status groups. (2) In blouse and two-piece there was a significant difference in placing importance on Practical dimension between socioeconomic status groups.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.567-576
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• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.