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| Main Author: | |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.23512 |
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| _version_ | 1866908993678475264 |
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| author | Mitra, Pradipta |
| author_facet | Mitra, Pradipta |
| contents | We propose a simple, projection-based algorithm for clustering mixtures of discrete (Bernoulli) distributions. Unlike previous approaches that rely on coordinate-specific ``combinatorial projections,'' our algorithm is rotationally invariant and works by projecting samples onto approximate centers obtained via a $k$-means computation on the best rank-$k$ approximation of the data matrix. This resolves a conjecture of McSherry on the existence of such geometric algorithms for discrete distributions. The same algorithm also applies to continuous distributions such as high-dimensional Gaussians, providing a unified approach across distribution types. We prove that the algorithm succeeds under a natural separation condition on the cluster centers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23512 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Simple Algorithm for Clustering Discrete Distributions Mitra, Pradipta Data Structures and Algorithms We propose a simple, projection-based algorithm for clustering mixtures of discrete (Bernoulli) distributions. Unlike previous approaches that rely on coordinate-specific ``combinatorial projections,'' our algorithm is rotationally invariant and works by projecting samples onto approximate centers obtained via a $k$-means computation on the best rank-$k$ approximation of the data matrix. This resolves a conjecture of McSherry on the existence of such geometric algorithms for discrete distributions. The same algorithm also applies to continuous distributions such as high-dimensional Gaussians, providing a unified approach across distribution types. We prove that the algorithm succeeds under a natural separation condition on the cluster centers. |
| title | A Simple Algorithm for Clustering Discrete Distributions |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2604.23512 |