Papers › Identifying Correlation in Stream of Samples
Identifying Correlation in Stream of Samples
Zhenhao Gu, Hao Zhang
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Identifying independence between two random variables or correlated given their samples has been a fundamental problem in Statistics. However, how to do so in a space-efficient way if the number of states is large is not quite well-studied. We propose a new, simple counter matrix algorithm, which utilize hash functions and a compressed counter matrix to give an unbiased estimate of the ℓ₂ independence metric. With 𝒪(ϵ⁻⁴logδ⁻¹) (very loose bound) space, we can guarantee 1±ϵ multiplicative error with probability at least 1-δ. We also provide a comparison of our algorithm with the state-of-the-art sketching of sketches algorithm and show that our algorithm is effective, and actually faster and at least 2 times more space-efficient.
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