Intro & related work

tex/chapters/experiments.tex

@@ -1,2 +1,27 @@
 \section{Experiments}
+We now empirically evaluate the accuracy of our method and compare its runtime performance with other state of the art approaches.
+To conclude our findings we present a real world example from a indoor localisation system.
 
+All tests are performed on a Intel Core \mbox{i5-7600K} CPU with a frequency of $4.5 \text{GHz}$, which supports the AVX2 instruction set, hence 256-bit wide SIMD registers are available.
+We compare our C++ implementation of the box filter based KDE to the KernSmooth R package and the \qq{FastKDE} implementation \cite{fastKDE}.
+The KernSmooth packages provides a FFT-based BKDE implementation based on optimized C functions at its core.
+
+\subsection{Error}
+In order to quantity the accuracy of our method the mean integrated squared error (MISE) is used.
+The ground truth is given as a synthetic data set drawn from a mixture normal density.
+Clearly, the choice of the ground truth distribution affects the resulting error.
+However, as our method approximates the KDE it is only of interest to evaluate the closeness to the KDE and not to the ground truth itself.
+Therefore, the particular choice of the ground truth is only of minor importance here.
+
+At first we evaluate the accuracy of our method as a function of the bandwidth $h$ in comparison to the exact KDE and the BKDE.
+
+
+
+% kde, box filter, exbox in abhänigkeit von h (bild)
+% sample size und grid size text
+% fastKDE fehler vergleich macht kein sinn weil kernel und bandbreite unterschiedlich sind
+
+
+\subsection{Performance}
+
+\subsection{Real World}

tex/chapters/introduction.tex

@@ -28,14 +28,19 @@ We formalize this ...
 Our experiments support our ..
 }
 
-In this paper, a novel approximation approach for rapid computation of the KDE is presented.
+
 %Therefore, this paper presents a novel approximation approach for rapid computation of the KDE. 
 %In this paper, a well known approximation of the Gaussian filter is used to speed up the computation of the KDE. 
+In this paper, a novel approximation approach for rapid computation of the KDE is presented.
+The basic idea is to interpret the estimation problem as a filtering operation.
+We show that computing the KDE with a Gaussian kernel on pre-binned data is equal to applying a Gaussian filter on the binned data.
+This allows us to use a well known approximation scheme for Gaussian filters using the box filter.
+Multiple recursion of a box filter yields an approximative Gaussian filter \cite{kovesi2010fast}.
 
- 
-
-
-
+This process converges quite fast to a reasonable close approximation of the ideal Gaussian.
+In addition, a box filter can be computed extremely fast by a computer, due to its intrinsic simplicity.
+While the idea to use several box filter passes to approximate a Gaussian has been around for a long, the application to obtain a fast KDE is new.
+% time sequential, fixed computation time, pre binned data!!
 
 % KDE wellknown nonparametic estimation method
 % Flexibility is paid with slow speed

tex/chapters/relatedwork.tex

@@ -1,5 +1,47 @@
 \section{Related work}
 % original work rosenblatt/parzen
+% langsam
+% other approaches Fast Gaussian Transform
 % binned version silverman, scott, härdle
 % -> Fourier transfom
-% other approaches Fast Gaussian Transform
+
+
+Kernel density estimation is well known non-parametric estimator, originally described independently by Rosenblatt \cite{rosenblatt1956remarks} and Parzen \cite{parzen1962estimation}.
+It was subject to extensive research and its theoretical properties are well understood.
+A comprehensive reference is given by Scott \cite{scott2015}.
+Although classified as non-parametric, the KDE has a two free parameters, the kernel function and its bandwidth.
+The selection of a \qq{good} bandwidth is still an open problem and heavily researched.
+However, the automatic selection of the bandwidth is not subject of this work and we refer to the literature \cite{turlach1993bandwidth}.
+
+The great flexibility of the KDE renders it very useful for many applications.
+However, its flexibility comes at the cost of a relative slow computation speed.
+The complexity of a naive implementation of the KDE is \landau{NM} evaluations of the kernel function, given $N$ data samples and $M$ points of the estimate.
+Therefore, a lot of effort was put into reducing the computation time of the KDE.
+Various methods have been proposed, which can be clustered based on different techniques.
+
+%  k-nearest neighbor searching
+An obvious way to speed up the computation is to reduce the number of evaluated kernel functions.
+One possible optimization is based on k-nearest neighbour search performed on spatial data structures.
+These algorithms reduce the number of evaluated kernels by taking the the spatial distance between clusters of data points into account \cite{gray2003nonparametric}.
+
+%  fast multipole method & Fast Gaus Transform 
+Another approach is to reduce the algorithmic complexity of the sum over Gaussian functions, by employing a specialized variant of the fast multipole method.
+The term fast Gauss transform was coined by Greengard \cite{greengard1991fast} who suggested this approach to reduce the complexity of the KDE to \label{N+M}.
+% However, the complexity grows exponentially with dimension. \cite{Improved Fast Gauss Transform and Efficient Kernel Density Estimation}
+
+% FastKDE, passed on ECF and nuFFT
+Recent methods based on the \qq{self-consistent} KDE proposed by Bernacchia and Pigolotti allow to obtain an estimate without any assumptions.
+They define a Fourier-based filter on the empirical characteristic function of a given dataset.
+The computation time was further reduced by \etal{O'Brien} using a non-uniform FFT algorithm to efficiently transform the data into Fourier space.
+Therefore, the data is not required to be on a grid.
+
+% binning => FFT
+In general, it is desirable to omit a grid, as the data points do not necessary fall onto equally spaced points.
+However, reducing the sample size by distributing the data on a equidistant grid can significantly reduce the computation time, if an approximative KDE is acceptable.
+Silverman \cite{silverman1982algorithm} originally suggested to combine adjacent data points into data bins and apply a FFT to quickly compute the estimate.
+This approximation scheme was later called binned KDE an was extensively studied \cite{fan1994fast} \cite{wand1994fast} \cite{hall1996accuracy} \cite{holmstrom2000accuracy}.
+
+The idea to approximate a Gaussian filter using several box filters was first formulated by Wells \cite{wells1986efficient}.
+Kovesi \cite{kovesi2010fast} suggested to use two box filter with different widths to increase accuracy maintaining the same complexity.
+To eliminate the approximation error completely \etal{Gwosdek} \cite{gwosdek2011theoretical} proposed a new approach called extended box filter.
+

tex/egbib.bib

@@ -2890,4 +2890,48 @@ year = {2003}
 }
 
 
+@inproceedings{kovesi2010fast,
+    title={Fast almost-gaussian filtering},
+    author={Kovesi, Peter},
+    booktitle={Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications},
+    pages={121--125},
+    year={2010},
+    publisher={IEEE}
+}
 
+@book{turlach1993bandwidth,
+    title={Bandwidth selection in kernel density estimation: A review},
+    author={Turlach, Berwin A.},
+    year={1993},
+    publisher={CORE and Institut de Statistique Universit{\'e} catholique de Louvain Louvain-la-Neuve}
+}
+
+@inproceedings{gray2003nonparametric,
+  title={Nonparametric density estimation: Toward computational tractability},
+  author={Gray, Alexander G and Moore, Andrew W},
+  booktitle={Proceedings of the 2003 SIAM International Conference on Data Mining},
+  pages={203--211},
+  year={2003},
+  organization={SIAM}
+}
+
+@article{greengard1991fast,
+  title={The fast Gauss transform},
+  author={Greengard, Leslie and Strain, John},
+  journal={SIAM Journal on Scientific and Statistical Computing},
+  volume={12},
+  number={1},
+  pages={79--94},
+  year={1991},
+  publisher={SIAM}
+}
+
+@article{wells1986efficient,
+    title={Efficient synthesis of Gaussian filters by cascaded uniform filters},
+    author={Wells, William M.},
+    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+    number={2},
+    pages={234--239},
+    year={1986},
+    publisher={IEEE}
+}