• Title, Summary, Keyword: principal component analysis (PCA)

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Evaluation of Water Quality Using Multivariate Statistic Analysis with Optimal Scaling

  • Kim, Sang-Soo;Jin, Hyun-Guk;Park, Jong-Soo;Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.349-357
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    • 2005
  • Principal component analysis(PCA) was carried out to evaluate the water quality with the monitering data collected from 1997 to 2003 along the coastal area of Ulsan, Korea. To enhance evaluation and to complement descriptive power of traditional PCA, optimal scaling was applied to transform the original data into optimally scaled data. Cluster analysis was also applied to classify the monitering stations according to their characteristics of water quality.

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Unsupervised Feature Selection Method Based on Principal Component Loading Vectors (주성분 분석 로딩 벡터 기반 비지도 변수 선택 기법)

  • Park, Young Joon;Kim, Seoung Bum
    • Journal of Korean Institute of Industrial Engineers
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    • v.40 no.3
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    • pp.275-282
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    • 2014
  • One of the most widely used methods for dimensionality reduction is principal component analysis (PCA). However, the reduced dimensions from PCA do not provide a clear interpretation with respect to the original features because they are linear combinations of a large number of original features. This interpretation problem can be overcome by feature selection approaches that identifying the best subset of given features. In this study, we propose an unsupervised feature selection method based on the geometrical information of PCA loading vectors. Experimental results from a simulation study demonstrated the efficiency and usefulness of the proposed method.

Improving the Subject Independent Classification of Implicit Intention By Generating Additional Training Data with PCA and ICA

  • Oh, Sang-Hoon
    • International Journal of Contents
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    • v.14 no.4
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    • pp.24-29
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    • 2018
  • EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

Novel assessment method of heavy metal pollution in surface water: A case study of Yangping River in Lingbao City, China

  • Liu, Yingran;Yu, Hongming;Sun, Yu;Chen, Juan
    • Environmental Engineering Research
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    • v.22 no.1
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    • pp.31-39
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    • 2017
  • The primary purpose of this research is to understand those elements that define heavy metals contamination and to propose a novel assessment method based on principal component analysis (PCA) in the Yangping River region of Lingbao City, China. This paper makes detailed calculations regarding such factors the single-factor assessment ($P_i$) and Nemerow's multi-factor index ($P_N$) of heavy metals found in the surface water of the Yangping River. The maximum values of $P_i$ (Cd) and $P_i$ (Pb) were determined to be 892.000 and 113.800 respectively. The maximum value of $P_N$ was calculated to be 639.836. The results of Pearson's correlation analysis, hierarchical cluster analysis, and PCA indicated heavy metal groupings as follows: Cu, Pb, Zn and As, Hg, Cd. The PCA-based pollution index ($P_{an}$) of samplings was subsequently calculated. The relative coefficient square was valued at 0.996 between $P_{an}$ and $P_N$, which indicated that $P_{an}$ is able to serve as a new heavy metal pollution index; not only this index able to eliminate the influence of the maximum value of $P_i$, but further, this index contains the principal component elements needed to evaluate heavy metal pollution levels.

Probabilistic penalized principal component analysis

  • Park, Chongsun;Wang, Morgan C.;Mo, Eun Bi
    • Communications for Statistical Applications and Methods
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    • v.24 no.2
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    • pp.143-154
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    • 2017
  • A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

A Penalized Principal Components using Probabilistic PCA

  • Park, Chong-Sun;Wang, Morgan
    • Proceedings of the Korean Statistical Society Conference
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    • pp.151-156
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    • 2003
  • Variable selection algorithm for principal component analysis using penalized likelihood method is proposed. We will adopt a probabilistic principal component idea to utilize likelihood function for the problem and use HARD penalty function to force coefficients of any irrelevant variables for each component to zero. Consistency and sparsity of coefficient estimates will be provided with results of small simulated and illustrative real examples.

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A Robust Principal Component Neural Network

  • Changha Hwang;Park, Hyejung;A, Eunyoung-N
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.625-632
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    • 2001
  • Principal component analysis(PCA) is a multivariate technique falling under the general title of factor analysis. The purpose of PCA is to Identify the dependence structure behind a multivariate stochastic observation In order to obtain a compact description of it. In engineering field PCA is utilized mainly (or data compression and restoration. In this paper we propose a new robust Hebbian algorithm for robust PCA. This algorithm is based on a hyperbolic tangent function due to Hampel ef al.(1989) which is known to be robust in Statistics. We do two experiments to investigate the performance of the new robust Hebbian learning algorithm for robust PCA.

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PCA Based Fault Diagnosis for the Actuator Process

  • Lee, Chang Jun
    • International Journal of Safety
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    • v.11 no.2
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    • pp.22-25
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    • 2012
  • This paper deals with the problem of fault diagnosis for identifying a single fault when the number of assumed faults is larger than that of predictive variables. Principal component analysis (PCA) is employed to isolate and identify a single fault. PCA is a method to extract important information as reducing the number of large dimension in a process. The patterns of all assumed faults can be recognized by PCA and these can be employed whether a new fault is one of predefined faults or not. Through PCA, empirical models for analyzing patterns can be trained. When a single fault occurs, the pattern generated by PCA can be obtained and this is used to identify a fault. The performance of the proposed approach is illustrated in the actuator benchmark problem.

Features for Figure Speech Recognition in Noise Environment (잡음환경에서의 숫자음 인식을 위한 특징파라메타)

  • Lee, Jae-Ki;Koh, Si-Young;Lee, Kwang-Suk;Hur, Kang-In
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • v.9 no.2
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    • pp.473-476
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    • 2005
  • This paper is proposed a robust various feature parameters in noise. Feature parameter MFCC(Mel Frequency Cepstral Coefficient) used in conventional speech recognition shows good performance. But, parameter transformed feature space that uses PCA(Principal Component Analysis)and ICA(Independent Component Analysis) that is algorithm transformed parameter MFCC's feature space that use in old for more robust performance in noise is compared with the conventional parameter MFCC's performance. The result shows more superior performance than parameter and MFCC that feature parameter transformed by the result ICA is transformed by PCA.

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Magnetocardiogram Topography with Automatic Artifact Correction using Principal Component Analysis and Artificial Neural Network

  • Ahn C.B.;Kim T.H.;Park H.C.;Oh S.J.
    • Journal of Biomedical Engineering Research
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    • v.27 no.2
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    • pp.59-63
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    • 2006
  • Magnetocardiogram (MCG) topography is a useful diagnostic technique that employs multi-channel magnetocardiograms. Measurement of artifact-free MCG signals is essenctial to obtain MCG topography or map for a diagnosis of human heart. Principal component analysis (PCA) combined with an artificial neural network (ANN) is proposed to remove a pulse-type artifact in the MCG signals. The algorithm is composed of a PCA module which decomposes the obtained signal into its principal components, followed by an ANN module for the classification of the components automatically. In the experiments with volunteer subjects, 97% of the decisions that were made by the ANN were identical to those by the human experts. Using the proposed technique, the MCG topography was successfully obtained without the artifact.