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<p>Low-rank binary matrix approximation (LRBMA) is a special case of matrix approximation. LRBMA is, in general, a NP-Hard problem. Given a binary matrix <i>A</i> low-rank binary matrix approximation is to find a matrix <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="42979_2025_4344_Article_IEq1.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(A^\prime\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>A</mi> <mo>′</mo> </msup> </math></EquationSource> </InlineEquation> such that it’s rank is less than or equal to a given constant. Several algorithms exist in the literature to solve this problem. Some of these are exponential in time complexity. We try to achieve the similar results in polynomial time complexity. As an application to the proposed algorithm Autism Spectrum Disorder Detection problem is considered. Results show that the proposed algorithm is comparable to the existing algorithms that have exponential time complexity.</p>

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Low-Rank Binary Matrix Approximation Using SVD Based Clustering Technique: Detecting Autism Spectrum Disorder (ASD)

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Narasimhulu Y,
  • Venkaiah V. China

摘要

Low-rank binary matrix approximation (LRBMA) is a special case of matrix approximation. LRBMA is, in general, a NP-Hard problem. Given a binary matrix A low-rank binary matrix approximation is to find a matrix \(A^\prime\) A ′ such that it’s rank is less than or equal to a given constant. Several algorithms exist in the literature to solve this problem. Some of these are exponential in time complexity. We try to achieve the similar results in polynomial time complexity. As an application to the proposed algorithm Autism Spectrum Disorder Detection problem is considered. Results show that the proposed algorithm is comparable to the existing algorithms that have exponential time complexity.