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New one-bit clustering method for sub-Gaussian mixture models proposed

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a novel one-bit clustering method designed for two-component sub-Gaussian mixture models. This technique utilizes a single bit per sample entry, processed through a dithered quantizer. The method demonstrates that a modified Lloyd's algorithm can achieve a misclassification rate that decreases exponentially with the signal-to-noise ratio, even in the presence of quantization. For high-dimensional data, a random rotation using a Haar distributed matrix can enforce the necessary non-spikiness condition, enabling exact recovery under specific separation conditions. AI

IMPACT Introduces a more efficient method for clustering potentially large datasets by reducing data requirements.

RANK_REASON The cluster contains an academic paper detailing a new statistical and machine learning method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New one-bit clustering method for sub-Gaussian mixture models proposed

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The cluster contains an academic paper detailing a new statistical and machine learning method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yun Yang ·

    One-Bit Clustering for Two Component Sub-Gaussian Mixture Models

    Clustering is a fundamental problem in statistics and machine learning. We propose the first one-bit clustering method for two-component sub-Gaussian mixture models. The method uses only one bit per entry of each sample obtained via a dithered quantizer. Under a mild non-spikines…

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