• Title/Summary/Keyword: Partial least squares (PLS)

Search Result 383, Processing Time 0.029 seconds

A Method for Screening Product Design Variables for Building A Usability Model : Genetic Algorithm Approach (사용편의성 모델수립을 위한 제품 설계 변수의 선별방법 : 유전자 알고리즘 접근방법)

  • Yang, Hui-Cheol;Han, Seong-Ho
    • Journal of the Ergonomics Society of Korea
    • /
    • v.20 no.1
    • /
    • pp.45-62
    • /
    • 2001
  • This study suggests a genetic algorithm-based partial least squares (GA-based PLS) method to select the design variables for building a usability model. The GA-based PLS uses a genetic algorithm to minimize the root-mean-squared error of a partial least square regression model. A multiple linear regression method is applied to build a usability model that contains the variables seleded by the GA-based PLS. The performance of the usability model turned out to be generally better than that of the previous usability models using other variable selection methods such as expert rating, principal component analysis, cluster analysis, and partial least squares. Furthermore, the model performance was drastically improved by supplementing the category type variables selected by the GA-based PLS in the usability model. It is recommended that the GA-based PLS be applied to the variable selection for developing a usability model.

  • PDF

Expressions for Shrinkage Factors of PLS Estimator

  • Kim, Jong-Duk
    • Journal of the Korean Data and Information Science Society
    • /
    • v.17 no.4
    • /
    • pp.1169-1180
    • /
    • 2006
  • Partial least squares regression (PLS) is a biased, non-least squares regression method and is an alternative to the ordinary least squares regression (OLS) when predictors are highly collinear or predictors outnumber observations. One way to understand the properties of biased regression methods is to know how the estimators shrink the OLS estimator. In this paper, we introduce an expression for the shrinkage factor of PLS and develop a new shrinkage expression, and then prove the equivalence of the two representations. We use two near-infrared (NIR) data sets to show general behavior of the shrinkage and in particular for what eigendirections PLS expands the OLS coefficients.

  • PDF

Prediction of Flash Point of Binary Systems by Using Multivariate Statistical Analysis (다변량 통계 분석법을 이용한 2성분계 혼합물의 인화점 예측)

  • Lee, Bom-Sock;Kim, S.Y.;Chung, C.B.;Choi, S.H.
    • Journal of the Korean Institute of Gas
    • /
    • v.10 no.4 s.33
    • /
    • pp.29-33
    • /
    • 2006
  • Estimation of process safety is important in the chemical process design. Prediction for flash points of flammable substances used in chemical processes is the one of the methods for estimating process safety. Flash point is the property used to examine the potential for the fire and explosion hazards of flammable substances. In this paper, multivariate statistical analysis methods(partial least squares(PLS) quadratic partial least squares(QPLS)) using experimental data is suggested for predicting flash points of flammable substances of binary systems. The prediction results are compared with the values calculated by laws of Raoult and Van Laar equation.

  • PDF

A Comparison of Estimation Approaches of Structural Equation Model with Higher-Order Factors Using Partial Least Squares (PLS를 활용한 고차요인구조 추정방법의 비교)

  • Son, Ki-Hyuk;Chun, Young-Ho;Ok, Chang-Soo
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.36 no.4
    • /
    • pp.64-70
    • /
    • 2013
  • Estimation approaches for casual relation model with high-order factors have strict restrictions or limits. In the case of ML (Maximum Likelihood), a strong assumption which data must show a normal distribution is required and factors of exponentiation is impossible due to the uncertainty of factors. To overcome this limitation many PLS (Partial Least Squares) approaches are introduced to estimate the structural equation model including high-order factors. However, it is possible to yield biased estimates if there are some differences in the number of measurement variables connected to each latent variable. In addition, any approach does not exist to deal with general cases not having any measurement variable of high-order factors. This study compare several approaches including the repeated measures approach which are used to estimate the casual relation model including high-order factors by using PLS (Partial Least Squares), and suggest the best estimation approach. In other words, the study proposes the best approach through the research on the existing studies related to the casual relation model including high-order factors by using PLS and approach comparison using a virtual model.

Simultaneous Kinetic Spectrophotometric Determination of Sulfite and Sulfide Using Partial Least Squares (PLS) Regression

  • Afkhami, Abbas;Sarlak, Nahid;Zarei, Ali Reza;Madrakian, Tayyebeh
    • Bulletin of the Korean Chemical Society
    • /
    • v.27 no.6
    • /
    • pp.863-868
    • /
    • 2006
  • The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of sulfite and sulfide is described. This method is based on the difference between the rate of the reaction of sulfide and sulfite with Malachite Green in pH 7.0 buffer solution and at 25 ${^{\circ}C}$. The absorption kinetic profiles of the solutions were monitored by measuring the decrease in the absorbance of Malachite Green at 617 nm in the time range 10-180 s after initiation of the reactions with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 24 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 0.030-1.5 and 0.030-1.2 $\mu$g m$L ^{-1}$ for sulfite and sulfide, respectively. The proposed method was successfully applied to simultaneous determination of sulfite and sulfide in water samples and whole human blood.

Unified Non-iterative Algorithm for Principal Component Regression, Partial Least Squares and Ordinary Least Squares

  • Kim, Jong-Duk
    • Journal of the Korean Data and Information Science Society
    • /
    • v.14 no.2
    • /
    • pp.355-366
    • /
    • 2003
  • A unified procedure for principal component regression (PCR), partial least squares (PLS) and ordinary least squares (OLS) is proposed. The process gives solutions for PCR, PLS and OLS in a unified and non-iterative way. This enables us to see the interrelationships among the three regression coefficient vectors, and it is seen that the so-called E-matrix in the solution expression plays the key role in differentiating the methods. In addition to setting out the procedure, the paper also supplies a robust numerical algorithm for its implementation, which is used to show how the procedure performs on a real world data set.

  • PDF

Multiple-Fault Diagnosis for Chemical Processes Based on Signed Digraph and Dynamic Partial Least Squares (부호유향그래프와 동적 부분최소자승법에 기반한 화학공정의 다중이상진단)

  • 이기백;신동일;윤인섭
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.9 no.2
    • /
    • pp.159-167
    • /
    • 2003
  • This study suggests the hybrid fault diagnosis method of signed digraph (SDG) and partial least squares (PLS). SDG offers a simple and graphical representation for the causal relationships between process variables. The proposed method is based on SDG to utilize the advantage that the model building needs less information than other methods and can be performed automatically. PLS model is built on local cause-effect relationships of each variable in SDG. In addition to the current values of cause variables, the past values of cause and effect variables are inputted to PLS model to represent the Process armies. The measured value and predicted one by dynamic PLS are compared to diagnose the fault. The diagnosis example of CSTR shows the proposed method improves diagnosis resolution and facilitates diagnosis of masked multiple-fault.

Combining Ridge Regression and Latent Variable Regression

  • Kim, Jong-Duk
    • Journal of the Korean Data and Information Science Society
    • /
    • v.18 no.1
    • /
    • pp.51-61
    • /
    • 2007
  • Ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS) are among popular regression methods for collinear data. While RR adds a small quantity called ridge constant to the diagonal of X'X to stabilize the matrix inversion and regression coefficients, PCR and PLS use latent variables derived from original variables to circumvent the collinearity problem. One problem of PCR and PLS is that they are very sensitive to overfitting. A new regression method is presented by combining RR and PCR and PLS, respectively, in a unified manner. It is intended to provide better predictive ability and improved stability for regression models. A real-world data from NIR spectroscopy is used to investigate the performance of the newly developed regression method.

  • PDF

Investigation of Partial Least Squares (PLS) Calibration Performance based on Different Resolutions of Near Infrared Spectra

  • Chung, Hoe-Il;Choi, Seung-Yeol;Choo, Jae-Bum;Lee, Young-Il
    • Bulletin of the Korean Chemical Society
    • /
    • v.25 no.5
    • /
    • pp.647-651
    • /
    • 2004
  • Partial Least Squares (PLS) calibration performance has been systematically investigated by changing spectral resolutions of near-infrared (NIR) spectra. For this purpose, synthetic samples simulating naphtha were prepared to examine the calibration performance in complex chemical matrix. These samples were composed of $C_6-C_9$ normal paraffin, iso-paraffin, naphthene, and aromatic hydrocarbons. NIR spectra with four different resolutions of 4, 8, 16, and 32$cm^{-1}$ were collected and then PLS regression was performed. For PLS calibration, five different group compositions (such as total paraffin content) and six different pure components (such as benzene concentration) were selected. The overall results showed that at least 8$cm^{-1}$ resolution was required to resolve the complex chemical matrix such as naphtha. It was found that the influence of resolution on the PLS calibration was varied by the spectral features of a component.

Missing Values Estimation for Time Course Gene Expression Data Using the Sequential Partial Least Squares Regression Fitting (순차적 부분최소제곱 회귀적합에 의한 시간경로 유전자 발현 자료의 결측치 추정)

  • Kim, Kyung-Sook;Oh, Mi-Ra;Baek, Jang-Sun;Son, Young-Sook
    • The Korean Journal of Applied Statistics
    • /
    • v.21 no.2
    • /
    • pp.275-290
    • /
    • 2008
  • The size of microarray gene expression data is very big and its observation process is also very complex. Thus missing values are frequently occurred. In this paper we propose the sequential partial least squares(SPLS) regression fitting method to estimate missing values for time course gene expression data that has correlations among observations over time points. The SPLS method is to combine the sequential technique with the partial least squares(PLS) regression fitting method. The usefulness of method proposed is evaluated through some simulation study for three yeast time course data.