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A scalable approach to spectral clustering with SDD solvers

机译:使用SDD求解器的频谱聚类的可扩展方法

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摘要

The promise of spectral clustering is that it can help detect complex shapes and intrinsic manifold structure in large and high dimensional spaces. The price for this promise is the expensive computational cost for computing the eigen-decomposition of the graph Laplacian matrix-so far a necessary subroutine for spectral clustering. In this paper we bypass the eigen-decomposition of the original Laplacian matrix by leveraging the recently introduced near-linear time solver for symmetric diagonally dominant (SDD) linear systems and random projection. Experiments on several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods.
机译:光谱聚类的前景是它可以帮助检测大尺寸和高尺寸空间中的复杂形状和固有流形结构。此承诺的代价是用于计算图拉普拉斯矩阵的本征分解的昂贵计算成本-到目前为止,这是频谱聚类的必要子例程。在本文中,我们通过利用最近引入的用于对称对角优势(SDD)线性系统和随机投影的近线性时间求解器来绕过原始Laplacian矩阵的本征分解。在几个综合和真实数据集上的实验表明,该方法具有更好的聚类质量,并且比最新的近似光谱聚类方法要快。

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