Partial least squares and random sample consensus in outlier detection

Analysis methods for plateau surfaces have been described in the ISO standards, JIS, and previous studies. The authors of a previous study proposed a method based on the concept of random sample consensus (RANSAC). This method achieved high analysis accuracy for plateau surfaces by setting detailed conditions. However, the process of setting optimal conditions is performed manually, which reduces productivity due to the manpower and man-hours required. In this study, we propose a new method for automating the setting of conditions. This method, which does not require human intervention, is expected to contribute to the improvement of productivity at production sites.

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Partial least squares with outlier detection in spectral analysis: A tool to predict gasoline properties

Fuel ◽

10.1016/j.fuel.2008.11.025 ◽

2009 ◽

Vol 88 (7) ◽

pp. 1216-1222 ◽

Cited By ~ 18

Author(s):

Xin Bao ◽

Liankui Dai

Keyword(s):

Spectral Analysis ◽

Least Squares ◽

Partial Least Squares ◽

Outlier Detection

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SMEs' adoption of enterprise applications

Journal of Small Business and Enterprise Development ◽

10.1108/jsbed-12-2011-0035 ◽

2013 ◽

Vol 20 (4) ◽

pp. 735-753 ◽

Cited By ~ 75

Author(s):

Boumediene Ramdani ◽

Delroy Chevers ◽

Densil A. Williams

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Random Sample ◽

Design Methodology ◽

Marketing Strategies ◽

Policy Makers ◽

Content Type ◽

Factors Influencing ◽

Enterprise Applications ◽

Practical Implications

Purpose – This paper aims to empirically explore the TOE (technology-organisation-environment) factors influencing small to medium-sized enterprises' (SMEs') adoption of enterprise applications (EA). Design/methodology/approach – Direct interviews were used to collect data from a random sample of SMEs located in the northwest of England. Using partial least squares (PLS) technique, 102 responses were analysed. Findings – Results indicate that technology, organisation and environment contexts impact SMEs' adoption of EA. This suggests that the TOE model is indeed a robust tool to predict the adoption of EA by SMEs. Research limitations/implications – Although this study focused on examining factors that influence SMEs' adoption of a set of systems such as CRM and e-procurement, it fails to differentiate between factors influencing each of these applications. The model used in this study can be used by software vendors not only in developing marketing strategies that can target potential SMEs, but also to develop strategies to increase the adoption of EA among SMEs. Practical implications – This model could be used by software vendors to determine which SMEs they should target with their products. It can also be used by policy makers to develop strategies to increase the rate of EA adoption among SMEs. Originality/value – This paper provides a model that can predict SMEs' adoption of EA. SMEs, adoption, enterprise applications, enterprise systems, ICT, PLS, technology-organisation-environment framework, TOE

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A New PLS and Bayesian Classification Based On-Line Outlier Detection Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.397-400.1362 ◽

2013 ◽

Vol 397-400 ◽

pp. 1362-1365 ◽

Cited By ~ 1

Author(s):

Tie Bin Wu ◽

Yun Cheng ◽

Zhi Kun Hu ◽

Wen Ping Xie ◽

Yun Lian Liu

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Outlier Detection ◽

Detection Method ◽

Bayesian Classification ◽

Partial Least Squares Analysis ◽

Sample Data ◽

On Line ◽

Simulation Results ◽

Lower Ratio

Considering that multivariate data is difficult to detect, this paper propose an PLS and Bayesian theory based on-line outlier detection method. Firstly, it figures out the Q-statistics by PLS(partial least squares analysis), then classify Q statistics with Bayesian classification method and decide whether or not the sample data is normal. We employ UCI database to verify the method, the simulation results show that, compared to traditional PCA based method, it has lower ratio of error judgement, and is more effective in detecting outliers and identifying the change of process states.

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