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

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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
    • 한국통계학회:학술대회논문집
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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.

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

  • 이재기;고시영;이광석;허강인
    • 한국정보통신학회:학술대회논문집
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    • v.9 no.2
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    • pp.473-476
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    • 2005
  • 본 논문은 잡음에 강한 다양한 특징 파라메타를 제안한다. 기존의 음성인식에서 사용되는 특징 파라메타 MFCC(Mel Frequency Cepstral Coeeficient)는 좋은 성능을 보인다. 그러나 잡음에 보다 강인한 성능을 위해 기존에 사용되는 파라메타 MFCC의 특징공간을 변형시키는 알고리즘인 PCA(Principal Component Analysis)와 ICA(Independent Component Analysis)를 사용하여 특징 공간을 변형시킨 파라메타와 기존의 파라메타 MFCC의 성능을 비교하였다. 그 결과 ICA에 의해 변형된 특징 파라메타가 PCA로 변형된 파라메타와 MFCC보다 우수한 성능을 보였다.

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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.
    • 대한의용생체공학회:의공학회지
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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.

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

  • 김상민;오광식;박희주
    • 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
  • 주성분 분석은 자료압축, 특징추출, 통신이론, 패턴인식 그리고 화상처리등의 컴퓨터 공학분야에서 중요하게 사용되고 있다. Oja(1982,1989,1992)는 확률적 경사 강하법(SGA:Stochastic Gradient Ascent)을 이용한 제한된 헵규칙을 제안하여 주성분 분석에 사용하였다. 그러나, 이 규칙은 이상치에 민감하므로 이상치의 영향을 줄이기 위해, Xu & Yuille(1995)는 통계물리 방법을 이용한 로버스트 에너지함수를 생성하여 로버스트 주성분 분석방법을 제안하였다. 또한 Devlin et.al(1981)은 M-추정량을 이용하여 주성분 분석을 하였다. 본 논문에서는 Oja(1992)의 규칙과 Xu & Yuille(1995)의 로버스트 에너지함수를 이용하여 신경망을 구성하였다. 그리고, Devlin et.al(1981)이 제안한 시뮬레이션조건하에서 실험을 하였다. 실험한 결과와 Devlin et.al(1981)의 결과를 비교, 분석함으로써, 신경망의 성능을 확인하고자 한다.

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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.