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

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On Robust Principal Component using Analysis Neural Networks (신경망을 이용한 로버스트 주성분 분석에 관한 연구)

  • Kim, Sang-Min;Oh, Kwang-Sik;Park, Hee-Joo
    • 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).

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Comparison of Significant Term Extraction Based on the Number of Selected Principal Components (주성분 보유수에 따른 중요 용어 추출의 비교)

  • Lee Chang-Beom;Ock Cheol-Young;Park Hyuk-Ro
    • The KIPS Transactions:PartB
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    • v.13B no.3
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    • pp.329-336
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    • 2006
  • In this paper, we propose a method of significant term extraction within a document. The technique used is Principal Component Analysis(PCA) which is one of the multivariate analysis methods. PCA can sufficiently use term-term relationships within a document by term-term correlations. We use a correlation matrix instead of a covariance matrix between terms for performing PCA. We also try to find out thresholds of both the number of components to be selected and correlation coefficients between selected components and terms. The experimental results on 283 Korean newspaper articles show that the condition of the first six components with correlation coefficients of |0.4| is the best for extracting sentence based on the significant selected terms.

Evaluating the Efficiency of Mobile Content Companies Using Data Envelopment Analysis and Principal Component Analysis

  • Cho, Eun-Jin;Park, Myeong-Cheol
    • ETRI Journal
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    • v.33 no.3
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    • pp.443-453
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    • 2011
  • This paper evaluates the efficiency of mobile content firms through a hybrid approach combining data envelopment analysis (DEA) to analyze the relative efficiency and performance of firms and principal component analysis (PCA) to analyze data structures. We performed a DEA using the total amount of assets, operating costs, employees, and years in business as inputs, and revenue as output. We calculated fifteen combinations of DEA efficiency in the mobile content firms. We performed a PCA on the results of the fifteen DEA models, dividing the mobile content firms into those having either 'asset-oriented' or 'manpower and experience-oriented' efficiency. Discriminant analysis was used to validate the relationship between the efficiency models and mobile content types. This paper contributes toward the construction of a framework that combines the DEA and PCA approaches in mobile content firms for use in comprehensive measurements. Such a framework has the potential to present major factors of efficiency for sustainable management in mobile content firms and to aid in planning mobile content industry policies.

The Recognition of Korean Syllables using Parameter Based on Principal Component Analysis (PCA 기반 파라메타를 이용한 숫자음 인식)

  • 박경훈;표창수;김창근;허강인
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • pp.181-184
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    • 2000
  • The new method of feature extraction is proposed, considering the statistic feature of human voice, unlike the conventional methods of voice extraction. PCA(principal Component Analysis) is applied to this new method. PCA removes the repeating of data after finding the axis direction which has the greatest variance in input dimension. Then the new method is applied to real voice recognition to assess performance. When results of the number recognition in this paper and the conventional Mel-Cepstrum of voice feature parameter are compared, there is 0.5% difference of recognition rate. Better recognition rate is expected than word or sentence recognition in that less convergence time than the conventional method in extracting voice feature. Also, better recognition tate is expected when the optimum vector is used by statistic feature of data.

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Analysis of Functional Connectivity in Human Working Memory using Positron Emission Tomography and Principal Component Analysis

  • Lee, J.S.;Ahn, J.Y.;Jang, M.J.;Lee, D.S.;Chung, J.K.;Lee, M.C.;Park, K.S.
    • Proceedings of the KOSOMBE Conference
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    • v.1998 no.11
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    • pp.257-258
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    • 1998
  • To reveal the interconnected brain regions involved in human working memory, their functional connectivity was analyzed using principal component analysis (PCA). rCBF PET scans were peformed on 5 normal volunteers during the verbal and visual working memory tasks and PCA was applied. PCA produced the first principal components related with the increase of the difficulty and the second one which demonstrate the dissociation of verbal and visual memory system.

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Image Classification Using Grey Block Distance Algorithms for Principal Component Analysis and Kurtosis (주성분분석과 첨도에서의 그레이 블록 거리 알고리즘을 이용한 영상분류)

  • Hong, Jun-Sik
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.779-782
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    • 2002
  • 본 논문에서는 주성분분석(principal component analysis; 이하 PCA) 및 첨도(Kurtosis)에서의 그레이 블록 거리 알고리즘(grey block algorithms; 이하 GBD)을 이용, 영상간의 거리를 측정하여 어느 정도 영상간의 상대적 식별을 용이하게 하여 영상 분류가 되는지 모의실험을 통하여 확인하고자 한다. 모의실험 결과로부터, PCA에서는 k가 9에서 상대적 식별이 불가능함을 보였고, 첨도에서는 k가 4까지만 블록을 택할 할 수 있음을 모의실험을 통하여 확인할 수 있었다.

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A novel method for predicting protein subcellular localization based on pseudo amino acid composition

  • Ma, Junwei;Gu, Hong
    • BMB Reports
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    • v.43 no.10
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    • pp.670-676
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    • 2010
  • In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical.

Robust Primary-ambient Signal Decomposition Method using Principal Component Analysis with Phase Alignment (위상 정렬을 이용한 주성분 분석법의 강인한 스테레오 음원 분리 성능유지 기법)

  • Baek, Yong-Hyun;Hyun, Dong-Il;Park, Young-Cheol
    • Journal of Broadcast Engineering
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    • v.19 no.1
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    • pp.64-74
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    • 2014
  • The primary and ambient signal decomposition of a stereo sound is a key step to the stereo upmix. The principal component analysis (PCA) is one of the most widely used methods of primary-ambient signal decomposition. However, previous PCA-based decomposition algorithms assume that stereo sound sources are only amplitude-panned without any consideration of phase difference. So it occurs some performance degradation in case of live recorded stereo sound. In this paper, we propose a new PCA-based stereo decomposition algorithm that can consider the phase difference between the channel signals. The proposed algorithm overcomes limitation of conventional signal model using PCA with phase alignment. The phase alignment is realized by using inter-channel phase difference (IPD) which is widely used in parametric stereo coding. Moreover, Enhanced Modified PCA(EMPCA) is combined to solve the problem of conventional PCA caused by Primary to Ambient energy Ratio(PAR) and panning angle dependency. The simulation results are presented to show the improvements of the proposed algorithm.

Principal Component Analysis Based Method for a Fault Diagnosis Model DAMADICS Process (주성분 분석을 이용한 DAMADICS 공정의 이상진단 모델 개발)

  • Park, Jae Yeon;Lee, Chang Jun
    • Journal of the Korean Society of Safety
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    • v.31 no.4
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    • pp.35-41
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    • 2016
  • In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.

Dimensional contraction by principal component analysis as preprocessing for independent component analysis at MCG

  • Iwai, M.;Kobayashi, K.
    • Biomedical Engineering Letters
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    • v.7 no.3
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    • pp.221-227
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    • 2017
  • We propose a noise reduction method for magnetocardiograms (MCGs) based on independent component analysis (ICA). ICA is useful to separate the noise and signal components, but ICA-based automatic noise reduction faces two main difficulties: the dimensional contraction process applied after the principal component analysis (PCA) used for preprocessing, and the component selection applied after ICA. The results of noise reduction vary among people, because these two processes typically depend on personal qualitative evaluations of the obtained components. Therefore, automatic quantitative ICA-based noise reduction is highly desirable. We will focus on the first difficulty, by improving the index used in the dimensional contraction process. The index used for component ordering after PCA affects the accuracy of separation obtained with ICA. The contribution ratio is often used as an index. However, its efficacy is highly dependent on the signal-to-noise ratio (SNR) it unsuitable for automation. We propose a kurtosis-based index, whose efficacy does not depend on SNR. We compare the two decision indexes through simulation. First, we evaluate their preservation rate of the MCG information after dimensional contraction. In addition, we evaluate their effect on the accuracy of the ICA-based noise reduction method. The obtained results show that the kurtosis-based index does preserve the MCG signal information through dimensional contraction, and has a more consistent behavior when the number of components increases. The proposed index performs better than the traditional index, especially in low SNRs. As such, it paves the way for the desired noise reduction process automation.