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

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Blind Source Separation via Principal Component Analysis

  • Choi, Seung-Jin
    • Journal of KIEE
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    • v.11 no.1
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    • pp.1-7
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    • 2001
  • Various methods for blind source separation (BSS) are based on independent component analysis (ICA) which can be viewed as a nonlinear extension of principal component analysis (PCA). Most existing ICA methods require certain nonlinear functions (which leads to higher-order statistics) depending on the probability distributions of sources, whereas PCA is a linear learning method based on second-order statistics. In this paper we show that the PCA can be applied to the task of BBS, provided that source are spatially uncorrelated but temporally correlated. Since the resulting method is based on only second-order statistics, it avoids the nonlinear function and is able to separate mixtures of several colored Gaussian sources, in contrast to the conventional ICA methods.

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Analyzing Exon Structure with PCA and ICA of Short-Time Fourier Transform

  • Hwang Changha;Sohn Insuk
    • Proceedings of the Korean Statistical Society Conference
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    • pp.79-84
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    • 2004
  • We use principal component analysis (PCA) to identify exons of a gene and further analyze their internal structures. The PCA is conducted on the short-time Fourier transform (STFT) based on the 64 codon sequences and the 4 nucleotide sequences. By comparing to independent component analysis (ICA), we can differentiate between the exon and intron regions, and how they are correlated in terms of the square magnitudes of STFTs. The experiment is done on the gene F56F11.4 in the chromosome III of C. elegans. For this data, the nucleotide based PCA identifies the exon and intron regions clearly. The codon based PCA reveals a weak internal structure in some exon regions, but not the others. The result of ICA shows that the nucleotides thymine (T) and guanine (G) have almost all the information of the exon and intron regions for this data. We hypothesize the existence of complex exon structures that deserve more detailed analysis.

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Principal Component Analysis on Marine Casualties Occurred at Korean Littoral Sea in Recent 5 Years (최근 5년간 국내 연근해에서 발생한 해양사고에 대한 주성분분석)

  • KIM, Yeong-Sik
    • Journal of Fisheries and Marine Sciences Education
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    • v.28 no.2
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    • pp.465-472
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    • 2016
  • Principal Component Analysis (PCA) is useful statistical technique for finding patterns in data, and expressing the data in such a way as to highlight their similarities and differences. In this paper, 1417 marine casualties occurred in Korean littoral sea in recent 5 years, were examined by the PCA. The main results obtained were as follows : 1. Most of marine casualties resulted from the human factors such as careless operation and insufficient engine maintenance. 2. Collision and standing mainly resulted from steering room-related human factors such as careless guard, inadequate ship-handling, however engine damage and fire explosion mainly resulted from engine room-related human factor such as bad handling of engine system. 3. No. 1 principal component represents accident frequency, No. 2 principal component represents the cause and No. 3 principal component represents the pattern of marine casualties, respectively.

New EM algorithm for Principal Component Analysis (주성분 분석을 위한 새로운 EM 알고리듬)

  • 안종훈;오종훈
    • Proceedings of the Korean Information Science Society Conference
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    • pp.529-531
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    • 2001
  • We present an expectation-maximization algorithm for principal component analysis via orthogonalization. The algorithm finds actual principal components, whereas previously proposed EM algorithms can only find principal subspace. New algorithm is simple and more efficient thant probabilistic PCA specially in noiseless cases. Conventional PCA needs computation of inverse of the covariance matrices, which makes the algorithm prohibitively expensive when the dimensions of data space is large. This EM algorithm is very powerful for high dimensional data when only a few principal components are needed.

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Face recognition rate comparison using Principal Component Analysis in Wavelet compression image (Wavelet 압축 영상에서 PCA를 이용한 얼굴 인식률 비교)

  • 박장한;남궁재찬
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.41 no.5
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    • pp.33-40
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    • 2004
  • In this paper, we constructs face database by using wavelet comparison, and compare face recognition rate by using principle component analysis (Principal Component Analysis : PCA) algorithm. General face recognition method constructs database, and do face recognition by using normalized size. Proposed method changes image of normalized size (92${\times}$112) to 1 step, 2 step, 3 steps to wavelet compression and construct database. Input image did compression by wavelet and a face recognition experiment by PCA algorithm. As well as method that is proposed through an experiment reduces existing face image's information, the processing speed improved. Also, original image of proposed method showed recognition rate about 99.05%, 1 step 99.05%, 2 step 98.93%, 3 steps 98.54%, and showed that is possible to do face recognition constructing face database of large quantity.

Structural Health Monitoring of Aircraft Reciprocating Engine Based on Principal Component Analysis (PCA) (주성분 분석(PCA)에 의한 항공기 왕복 엔진의 구조 건전도 모니터링)

  • Kim, Ji-Hwan;Park, Seong-Eun;Lee, Hyeong-Cheol
    • Journal of Aerospace System Engineering
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    • v.6 no.1
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    • pp.13-18
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    • 2012
  • This paper presents a structural health monitoring method of aircraft reciprocating engine using Principal Component Analysis (PCA) which analyzes vibration expressed by Averaged Normalized Power Spectral Density (ANPSD). Because ANPSD of the rotating shaft is sensitive to the rotating speed, this paper proposes to use a post-processing method of ANPSD is used to reduce the sensitivity. The PCA extracts compressed information from the post-processed ANPSDs and the information means the difference between current and normal cases of the engine. The experimental results demonstrate the feasibility and effectiveness of the proposed method to detect abnormal cases of the engine.

Joint Exponential Smoothing and Trend-based Principal Component Analysis for Anomaly Detection in Wireless Sensor Networks (무선 센서 네트워크에서의 이상 징후 감지를 위한 공동 지수 평활법 및 추세 기반 주성분 분석)

  • Dang, Thien-Binh;Yang, Hui-Gyu;Tran, Manh-Hung;Le, Duc-Tai;Kim, Moonseong;Choo, Hyunseung
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.145-148
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    • 2019
  • Principal Component Analysis (PCA) is a powerful technique in data analysis and widely used to detect anomalies in Wireless Sensor Networks. However, the performance of conventional PCA is not high on time-series data collected by sensors. In this paper, we propose a Joint Exponential Smoothing and Trend-based Principal Component Analysis (JES-TBPCA) for Anomaly Detection which is based on conventional PCA. Experimental results on a real dataset show a remarkably higher performance of JES-TBPCA comparing to conventional PCA model in detection of stuck-at and offset anomalies.

Face Recognition Based on PCA on Wavelet Subband of Average-Half-Face

  • Satone, M.P.;Kharate, G.K.
    • Journal of Information Processing Systems
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    • v.8 no.3
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    • pp.483-494
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    • 2012
  • Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition appears to offer several advantages over other biometric methods. Nowadays, Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly.

Arrow Diagrams for Kernel Principal Component Analysis

  • Huh, Myung-Hoe
    • Communications for Statistical Applications and Methods
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    • v.20 no.3
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    • pp.175-184
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    • 2013
  • Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.

Assessment of water quality variations under non-rainy and rainy conditions by principal component analysis techniques in Lake Doam watershed, Korea

  • Bhattrai, Bal Dev;Kwak, Sungjin;Heo, Woomyung
    • Journal of Ecology and Environment
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    • v.38 no.2
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    • pp.145-156
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    • 2015
  • This study was based on water quality data of the Lake Doam watershed, monitored from 2010 to 2013 at eight different sites with multiple physiochemical parameters. The dataset was divided into two sub-datasets, namely, non-rainy and rainy. Principal component analysis (PCA) and factor analysis (FA) techniques were applied to evaluate seasonal correlations of water quality parameters and extract the most significant parameters influencing stream water quality. The first five principal components identified by PCA techniques explained greater than 80% of the total variance for both datasets. PCA and FA results indicated that total nitrogen, nitrate nitrogen, total phosphorus, and dissolved inorganic phosphorus were the most significant parameters under the non-rainy condition. This indicates that organic and inorganic pollutants loads in the streams can be related to discharges from point sources (domestic discharges) and non-point sources (agriculture, forest) of pollution. During the rainy period, turbidity, suspended solids, nitrate nitrogen, and dissolved inorganic phosphorus were identified as the most significant parameters. Physical parameters, suspended solids, and turbidity, are related to soil erosion and runoff from the basin. Organic and inorganic pollutants during the rainy period can be linked to decayed matters, manure, and inorganic fertilizers used in farming. Thus, the results of this study suggest that principal component analysis techniques are useful for analysis and interpretation of data and identification of pollution factors, which are valuable for understanding seasonal variations in water quality for effective management.