Image processing news

Recent days brought to us two interesting articles about image processing. First one describes wavelets as a tool to analyze spectral and spatial informations inside images [1]. The second one presents immortal topic - a new idea of 2D electrophoretic images alignment [2]. Enjoy the lecture!

References

  1. P. Juneau, A. Garnier, and C. Duchesne, "The undecimated wavelet transform–multivariate image analysis (UWT-MIA) for simultaneous extraction of spectral and spatial information", Chemometrics and Intelligent Laboratory Systems, 2014. http://dx.doi.org/10.1016/j.chemolab.2014.09.007
  2. S. Nhek, E. Mosleth, M. Høy, M. Griessl, B. Tessema, U. Indahl, and H. Martens, "Nonlinear visualisation and pixel-based alignment of 2D electrophoresis images", Chemometrics and Intelligent Laboratory Systems, vol. 118, pp. 97-108, 2012. http://dx.doi.org/10.1016/j.chemolab.2012.08.008

Fuzzy and crisp data (with robust clustering...)

There are some robust methods of clustering: possibilistic, noise, metric, trimmed, semifuzzy, etc... How to transfer them to fuzzy data? A new Chemolab article [1] gives the discussion and some interesting proposal. I recommend this cited reference [2] for future reading.

References

  1. P. D'Urso, and L. De Giovanni, "Robust clustering of imprecise data", Chemometrics and Intelligent Laboratory Systems, vol. 136, pp. 58-80, 2014. http://dx.doi.org/10.1016/j.chemolab.2014.05.004
  2. R. Dave, and R. Krishnapuram, "Robust clustering methods: a unified view", IEEE Transactions on Fuzzy Systems, vol. 5, pp. 270-293, 1997. http://dx.doi.org/10.1109/91.580801

New articles about variable selection

Interested in variable/feature selection? Last weeks brought to us three interesting papers: a study on PLS with autororrelated data [1], general multinomial logit models [2] and subsampling approach [3].

References

  1. N. Afanador, T. Tran, L. Blanchet, and L. Buydens, "Variable Importance in PLS in the Presence of Autocorrelated Data - Case studies in Manufacturing Processes", Chemometrics and Intelligent Laboratory Systems, 2014. http://dx.doi.org/10.1016/j.chemolab.2014.09.008
  2. G. Tutz, W. Pößnecker, and L. Uhlmann, "Variable selection in general multinomial logit models", Computational Statistics & Data Analysis, vol. 82, pp. 207-222, 2015. http://dx.doi.org/10.1016/j.csda.2014.09.009
  3. Z. Lin, X. Pan, B. Xu, J. Zhang, X. Shi, and Y. Qiao, "Evaluating the reliability of spectral variables selected by subsampling methods", Journal of Chemometrics, pp. n/a-n/a, 2014. http://dx.doi.org/10.1002/cem.2667

OCPLS in Matlab

One-class partial least squares (OCPLS) classifiers were implemented in new Matlab Toolbox [1]. What are one class classifiers? The answer could be find in this often cited article [2].

References

  1. L. Xu, M. Goodarzi, W. Shi, C. Cai, and J. Jiang, "A MATLAB toolbox for class modeling using one-class partial least squares (OCPLS) classifiers", Chemometrics and Intelligent Laboratory Systems, 2014. http://dx.doi.org/10.1016/j.chemolab.2014.09.005
  2. R.G. Brereton, "One-class classifiers", Journal of Chemometrics, vol. 25, pp. 225-246, 2011. http://dx.doi.org/10.1002/cem.1397

Baseline in chromatographic signals

The baseline problem is still important in processing of chromatograms. Many methods are proposed for baseline filtering [1]. In recent Chemolab an interesting algorithm for baseline and denoising, called BEADS [2] is proposed.

References

  1. . Komsta, "Comparison of Several Methods of Chromatographic Baseline Removal with a New Approach Based on Quantile Regression", Chromatographia, vol. 73, pp. 721-731, 2011. http://dx.doi.org/10.1007/s10337-011-1962-1
  2. X. Ning, I.W. Selesnick, and L. Duval, "Chromatogram baseline estimation and denoising using sparsity (BEADS)", Chemometrics and Intelligent Laboratory Systems, 2014. http://dx.doi.org/10.1016/j.chemolab.2014.09.014

Biobjective sparse PCA

Sparse PCA is an idea to explain maximum variance with as few of original variables, as possible, increasing the interpretability. Spanish team presents in new JMVA [1] new heuristic approach of maximization of variance explained and sparseness simultaneously. If the reader knows PCA, but has no knowledge on sparse PCA, this article [2] is a "must read".

References

  1. E. Carrizosa, and V. Guerrero, "Biobjective sparse principal component analysis", Journal of Multivariate Analysis, vol. 132, pp. 151-159, 2014. http://dx.doi.org/10.1016/j.jmva.2014.07.010
  2. H. Zou, T. Hastie, and R. Tibshirani, "Sparse Principal Component Analysis", Journal of Computational and Graphical Statistics, vol. 15, pp. 265-286, 2006. http://dx.doi.org/10.1198/106186006X113430

Chemical rank of trilinear data

Trilinear data (such as fluorescence spectra) are very interesting subject of chemometric analysis. New paper by Li et al. [1] contains an interesting proposal for rank estimation of such data, called vector subspace projection with Monte Carlo simulation (VSPMCS). Beginners in tri- and multilinearity can start with a great review of A. Olivieri [2].

References

  1. Y. Li, H. Wu, X. Zhang, Y. Chen, H. Gu, Q. Zuo, Y. Zhang, S. Guo, X. Liu, and R. Yu, "Estimating the chemical rank of three-way fluorescence data by vector subspace projection with Monte Carlo simulation", Chemometrics and Intelligent Laboratory Systems, vol. 136, pp. 15-23, 2014. http://dx.doi.org/10.1016/j.chemolab.2014.05.005
  2. A.C. Olivieri, "Analytical Advantages of Multivariate Data Processing. One, Two, Three, Infinity?", Anal. Chem., vol. 80, pp. 5713-5720, 2008. http://dx.doi.org/10.1021/ac800692c

Chromatographic signals alignment

In a recent article in JSS [1], French authors discuss theoretically chromatographic alighment methods: COW, PTW, PARS, PAGA, PABS, Defferential Evolution, PAFFT, RAFFT, SPTW and compare their performance. This interesting article can be a good starting point for the reader interested in warping. And this chapter [2] can be a good companion.

References

  1. R. Korifi, Y.L. Dréau, and N. Dupuy, "Comparative study of the alignment method on experimental and simulated chromatographic data", Journal of Separation Science, pp. n/a-n/a, 2014. http://dx.doi.org/10.1002/jssc.201400700
  2. R. Jellema, "Variable Shift and Alignment", Comprehensive Chemometrics, pp. 85-108, 2009. http://dx.doi.org/10.1016/B978-044452701-1.00104-6