374
Views
2
CrossRef citations to date
0
Altmetric
Quality & Reliability Engineering

A spatiotemporal outlier detection method based on partial least squares discriminant analysis and area Delaunay triangulation for image-based process monitoring

, &
Pages 74-87 | Received 24 Apr 2016, Accepted 26 Sep 2017, Published online: 08 Jan 2018

References

  • Aggarwal, C.C. (2013) Outlier Analysis, Springer, New York, NY.
  • Aggarwal, C.C. and Yu, P.S. (2001) Outlier detection for high dimensional data. ACM Sigmod Record, 30(2), 37–46.
  • Anselin, L. (1995) Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.
  • Anselin, L. (1999) Interactive techniques and exploratory spatial data analysis. Geographical Information Systems: Principles, Techniques, Management, and Applications, 1, 251–264.
  • Barker, M. and Rayens, W. (2003) Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166–173.
  • Barnett, V. and Lewis, T. (1994) Outliers in Statistical Data, Wiley, New York, NY.
  • Bharati, M.H., Liu, J.J. and MacGregor, J.F. (2004) Image texture analysis: Methods and comparisons. Chemometrics and Intelligent Laboratory Systems, 72(1), 57–71.
  • Du, Z., Jeong, M.K. and Kong, S.G. (2007) Band selection of hyperspectral images for automatic detection of poultry skin tumors. IEEE Transactions on Automation Science and Engineering, 4(3), 332–339.
  • Duchesne, C., Liu, J.J. and MacGregor, J.F. (2012) Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116–128.
  • Dvoretzky, A. (1972) Asymptotic normality for sums of dependent random variables, in Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 2: Probability Theory, The Regents of the University of California, Oakland, CA.
  • Geladi, P. and Kowalski, B.R. (1986) Partial least-squares regression: A tutorial. Analytica Chimica Acta, 185, 1–17.
  • Grasedyck, L., Kressner, D. and Tobler, C. (2013) A literature survey of low-rank tensor approximation techniques. GAMM-Mitteilungen, 36(1), 53–78.
  • Gupta, M., Gao, J., Aggarwal, C.C. and Han, J. (2014) Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering, 26(9), 2250–2267.
  • Jiang, B.C., Wang, C.C. and Liu, H.C. (2005) Liquid crystal display surface uniformity defect inspection using analysis of variance and exponentially weighted moving average techniques. International Journal of Production Research, 43(1), 67–80.
  • Johnson, R.A. and Wichern, D.W. (1992) Applied Multivariate Statistical Analysis, Vol. 4, Prentice Hall, Englewood Cliffs, NJ.
  • Kan, C. and Yang, H. (2015) Network models for monitoring high-dimensional image profiles, in Proceedings of the 2015 IEEE International Conference on Automation Science and Engineering, IEEE Press, Piscataway, NY, pp. 1078–1083.
  • Kapur, K.C. and Pecht, M. (2014) Reliability Engineering, John Wiley & Sons, Hoboken, NJ.
  • Kim, K., Lee, J.M. and Lee, I.B. (2005) A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction. Chemometrics and Intelligent Laboratory Systems, 79(1), 22–30.
  • Kim, K., Mahmoud, M.A. and Woodall, W.H. (2003) On the monitoring of linear profiles. Journal of Quality Technology, 35(3), 317–328.
  • Knorr, E.M. and Ng, R.T. (1998) Algorithms for mining distance-based outliers in large datasets, in Proceedings of the International Conference on Very Large Data Bases, pp. 392–403, Morgan Kaufmann Publishers, San Francisco, CA.
  • Knorr, E.M. and Ng, R.T. (1999) Finding intensional knowledge of distance-based outliers. Very Large Data Bases, 99, 211–222.
  • Kut, A. and Birant, D. (2006) Spatiotemporal outlier detection in large databases. Journal of Computing and Information Technology, 14(4), 291–297.
  • Lee, D.T. and Schachter, B.J. (1980) Two algorithms for constructing a Delaunay triangulation. International Journal of Computer & Information Sciences, 9(3), 219–242.
  • Lin, H.D., Chung, C.Y. and Lin, W.T. (2008) Principal component analysis based on wavelet characteristics applied to automated surface defect inspection. WSEAS Transactions on Computer Research, 3(4), 193–202.
  • Liu, J.J. and MacGregor, J.F. (2007) On the extraction of spectral and spatial information from images. Chemometrics and Intelligent Laboratory Systems, 85(1), 119–130.
  • Lu, C.J. and Tsai, D.M. (2005) Automatic defect inspection for LCDs using singular value decomposition. The International Journal of Advanced Manufacturing Technology, 25(1–2), 53–61.
  • Malamas, E.N., Petrakis, E.G., Zervakis, M., Petit, L. and Legat, J.D. (2003) A survey on industrial vision systems, applications and tools. Image and Vision Computing, 21(2), 171–188.
  • McLeish, D.L. (1974) Dependent central limit theorems and invariance principles. The Annals of Probability, 2(4), 620–628.
  • Megahed, F.M., Wells, L.J., Camelio, J.A. and Woodall, W.H. (2012) A spatiotemporal method for the monitoring of image data. Quality and Reliability Engineering International, 28(8), 967–980.
  • Megahed, F.M., Woodall, W.H. and Camelio, J.A. (2011) A review and perspective on control charting with image data. Journal of Quality Technology, 43(2), 83–98.
  • Mei, Y. (2011) Quickest detection in censoring sensor networks, in Proceedings of the 2011 IEEE International Symposium on Information Theory, IEEE Press, Piscataway, NJ, pp. 2148–2152.
  • Park, C., Huang, J.Z., Huitink, D., Kundu, S., Mallick, B.K., Liang, H. and Ding, Y. (2012) A multistage, semi-automated procedure for analyzing the morphology of nanoparticles. IIE Transactions, 44(7), 507–522.
  • Preparata, F.P. and Shamos, M.I. (1985) Computational Geometry: An Introduction, Springer, New York, NY.
  • Ramaswamy, S., Rastogi, R. and Shim, K. (2000) Efficient algorithms for mining outliers from large data sets. ACM SIGMOD Record, 29(2), 427–438.
  • Ruts, I. and Rousseeuw, P.J. (1996) Computing depth contours of bivariate point clouds. Computational Statistics & Data Analysis, 23(1), 153–168.
  • Sbárbaro, D. and Villar, R. (2010) Advanced Control and Supervision of Mineral Processing Plants, Springer Science & Business Media, Berlin, Germany.
  • Shekhar, S., Lu, C.T. and Zhang, P. (2001) A unified approach to spatial outlier detection. Report, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN.
  • Suits, D.B. (1957) Use of dummy variables in regression equations. Journal of the American Statistical Association, 52(280), 548–551.
  • Tartakovsky, A.G., Rozovskii, B.L., Blažek, R.B. and Kim, H. (2006) Detection of intrusions in information systems by sequential change-point methods. Statistical Methodology, 3(3), 252–293.
  • Wang, K. and Tsung, F. (2005) Using profile monitoring techniques for a data-rich environment with huge sample size. Quality and Reliability Engineering International, 21(7), 677–688.
  • Wang, Z.Q., Wang, S.K., Hong, T. and Wan, X.H. (2004) A spatial outlier detection algorithm based multi-attributive correlation, in Proceedings of the 2004 International Conference on Machine Learning and Cybernetics, IEEE Press, Piscataway, NJ, pp. 1727–1732.
  • Wold, H. (1966) Estimation of principal components and related models by iterative least squares. Multivariate Analysis, 1, 391–420.
  • Wold, S., Kettaneh-Wold, N. and Skagerberg, B. (1989) Nonlinear PLS modeling. Chemometrics and Intelligent Laboratory Systems, 7, 53–65.
  • Wood, B.R., Bambery, K.R., Evans, C.J., Quinn, M.A. and McNaughton, D. (2006) A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections. BMC Medical Imaging, 6(1), 1–9.
  • Xue, A. and Ju, S. (2006) Algorithm for spatial outlier detection based on outlying degree, in Proceedings of the Sixth World Congress on Intelligent Control and Automation, Vol. 2, pp. 6005–6009, Piscataway, NJ, IEEE.
  • Yan, H., Paynabar, K. and Shi, J. (2015) Image-based process monitoring using low-rank tensor decomposition. IEEE Transactions on Automation Science and Engineering, 12(1), 216–227.
  • Yan, H., Paynabar, K. and Shi, J. (2017) Anomaly detection in images with smooth background via smooth-sparse decomposition. Technometrics, 59(1), 102–114.
  • Ye, J., Janardan, R. and Li, Q. (2004) GPCA: An efficient dimension reduction scheme for image compression and retrieval, in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 354–363, ACM, New York, NY.
  • Yeung, K.Y. and Ruzzo, W.L. (2001) Principal component analysis for clustering gene expression data. Bioinformatics, 17(9), 763–774.
  • Yu, D., Sheikholeslami, G. and Zhang, A. (1999) Find-Out: Finding outliers in very large datasets. Technical report 99-03, Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY.
  • Yu, H. and MacGregor, J.F. (2003) Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods. Chemometrics and Intelligent Laboratory Systems, 67(2), 125–144.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.