<p>Computations in high-dimensional spaces can often be realized only approximately, using a certain number of projections onto lower dimensional subspaces or sampling from distributions. In this paper, we are interested in pairs of real-valued functions (<i>F</i>,&#xa0;<i>f</i>) on <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="43670_2025_100_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="45" /> </InlineMediaObject> <EquationSource Format="TEX">\([0,\infty )\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">[</mo> <mn>0</mn> <mo>,</mo> <mi>∞</mi> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation> that are related by the projection/slicing formula <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="43670_2025_100_Article_IEq2.gif" Format="GIF" Height="23" Rendition="HTML" Resolution="72" Type="Linedraw" Width="181" /> </InlineMediaObject> <EquationSource Format="TEX">\(F (\Vert x \Vert ) = {\mathbb {E}}_{\xi } \big [ f \big (|\langle x,\xi \rangle | \big ) \big ]\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>F</mi> <mrow> <mo stretchy="false">(</mo> <mo stretchy="false">‖</mo> <mi>x</mi> <mo stretchy="false">‖</mo> <mo stretchy="false">)</mo> </mrow> <mo>=</mo> <msub> <mi mathvariant="double-struck">E</mi> <mi>ξ</mi> </msub> <mrow> <mo maxsize="1.2em" minsize="1.2em" stretchy="true">[</mo> </mrow> <mi>f</mi> <mrow> <mo maxsize="1.2em" minsize="1.2em" stretchy="true">(</mo> </mrow> <mrow> <mo stretchy="false">|</mo> <mrow> <mo stretchy="false">⟨</mo> <mi>x</mi> <mo>,</mo> <mi>ξ</mi> <mo stretchy="false">⟩</mo> </mrow> <mo stretchy="false">|</mo> </mrow> <mrow> <mo maxsize="1.2em" minsize="1.2em" stretchy="true">)</mo> </mrow> <mrow> <mo maxsize="1.2em" minsize="1.2em" stretchy="true">]</mo> </mrow> </mrow> </math></EquationSource> </InlineEquation> for <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="43670_2025_100_Article_IEq3.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="51" /> </InlineMediaObject> <EquationSource Format="TEX">\(x\in {\mathbb {R}}^d\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>x</mi> <mo>∈</mo> <msup> <mrow> <mi mathvariant="double-struck">R</mi> </mrow> <mi>d</mi> </msup> </mrow> </math></EquationSource> </InlineEquation>, where the expectation value is taken over uniformly distributed directions in <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="43670_2025_100_Article_IEq4.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="20" /> </InlineMediaObject> <EquationSource Format="TEX">\({\mathbb {R}}^d\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mi mathvariant="double-struck">R</mi> </mrow> <mi>d</mi> </msup> </math></EquationSource> </InlineEquation>. While it is known that <i>F</i> can be obtained from <i>f</i> by an Abel-like integral formula, we construct conversely <i>f</i> from given <i>F</i> using their Fourier transforms. First, we consider the relation between <i>F</i> and <i>f</i> for radial functions <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="43670_2025_100_Article_IEq5.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="57" /> </InlineMediaObject> <EquationSource Format="TEX">\(F(\Vert \cdot \Vert )\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>F</mi> <mo stretchy="false">(</mo> <mo stretchy="false">‖</mo> <mo>·</mo> <mo stretchy="false">‖</mo> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation> that are Fourier transforms of <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="43670_2025_100_Article_IEq6.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(L^1\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>L</mi> <mn>1</mn> </msup> </math></EquationSource> </InlineEquation> functions. Besides <i>d</i>- and one-dimensional Fourier transforms, it relies on a rotation operator, an averaging operator and a multiplication operator to manage the walk from <i>d</i> to one dimension in the Fourier space. Then, we generalize the results to tempered distributions, where we are mainly interested in radial regular tempered distributions. Based on Bochner’s theorem, this includes positive definite functions <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="43670_2025_100_Article_IEq5.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="57" /> </InlineMediaObject> <EquationSource Format="TEX">\(F(\Vert \cdot \Vert )\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>F</mi> <mo stretchy="false">(</mo> <mo stretchy="false">‖</mo> <mo>·</mo> <mo stretchy="false">‖</mo> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation> and, by the theory of fractional derivatives, also functions <i>F</i> whose derivative of order <InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="43670_2025_100_Article_IEq8.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="39" /> </InlineMediaObject> <EquationSource Format="TEX">\(\lfloor \nicefrac {d}{2}\rfloor \)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>⌊</mo> <mfrac bevelled="true"> <mi>d</mi> <mn>2</mn> </mfrac> <mo>⌋</mo> </mrow> </math></EquationSource> </InlineEquation> is slowly increasing and continuous.</p>

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Slicing of radial functions: a dimension walk in the Fourier space

  • Nicolaj Rux,
  • Michael Quellmalz,
  • Gabriele Steidl

摘要

Computations in high-dimensional spaces can often be realized only approximately, using a certain number of projections onto lower dimensional subspaces or sampling from distributions. In this paper, we are interested in pairs of real-valued functions (Ff) on \([0,\infty )\) [ 0 , ) that are related by the projection/slicing formula \(F (\Vert x \Vert ) = {\mathbb {E}}_{\xi } \big [ f \big (|\langle x,\xi \rangle | \big ) \big ]\) F ( x ) = E ξ [ f ( | x , ξ | ) ] for \(x\in {\mathbb {R}}^d\) x R d , where the expectation value is taken over uniformly distributed directions in \({\mathbb {R}}^d\) R d . While it is known that F can be obtained from f by an Abel-like integral formula, we construct conversely f from given F using their Fourier transforms. First, we consider the relation between F and f for radial functions \(F(\Vert \cdot \Vert )\) F ( · ) that are Fourier transforms of \(L^1\) L 1 functions. Besides d- and one-dimensional Fourier transforms, it relies on a rotation operator, an averaging operator and a multiplication operator to manage the walk from d to one dimension in the Fourier space. Then, we generalize the results to tempered distributions, where we are mainly interested in radial regular tempered distributions. Based on Bochner’s theorem, this includes positive definite functions \(F(\Vert \cdot \Vert )\) F ( · ) and, by the theory of fractional derivatives, also functions F whose derivative of order \(\lfloor \nicefrac {d}{2}\rfloor \) d 2 is slowly increasing and continuous.