Browsing by Subject "Dimension reduction"

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  • Niskanen, Vesa A. (Springer-Verlag, 2017)
    Studies in Computational Intelligence
    A rapid soft computing method for dimensionality reduction of data sets is presented. Traditional approaches usually base on factor or principal component analysis. Our method applies fuzzy cluster analysis and approximate reasoning instead, and thus it is also viable to nonparametric and nonlinear models. Comparisons are drawn between the methods with two empiric data sets.
  • Hakkarainen, Janne; Purisha, Zenith; Solonen, Antti; Siltanen, Samuli (2019)
    In this paper, we propose a prior-based dimension reduction Kalman filter for undersampled dynamic X-ray tomography. With this method, the X-ray reconstructions are parameterized by a low-dimensional basis. Thus, the proposed method is computationally very light, and extremely robust as all the computations can be done explicitly. With real and simulated measurement data, we show that the method provides accurate reconstructions even with very limited number of angular directions.