Entropy-Punctured Bloom Filters for Memory-Efficient Machine Learning

2026-09-12

AI Summary

本文提出了一种基于熵的穿孔布隆过滤器方法，通过去除低变异位来压缩特征表示，在保持预测性能的同时减少存储需求，适用于内存受限的机器学习场景。

Abstract

Memory-efficient feature representations are increasingly important in machine learning settings where storage, transmission cost, bandwidth, or privacy constraints limit access to raw data. Bloom Filter (BF) encodings provide compact probabilistic representations of engineered features, but their behavior under structural compression and their applicability to regression tasks remain underexplored. In this work, we propose entropy-punctured Bloom Filters, a memory-aware encoding strategy that removes low-variability bit positions identified using empirical entropy. Starting from fixed-length BF encodings of quantized features, the proposed approach produces reduced representations that preserve predictive structure while improving predictive efficiency relative to encoded representation size. We evaluate the approach on diverse regression datasets, comparing raw features, Principal Component Analysis (PCA), Random Projection (RP), and Bloom Filter variants under leakage-free evaluation protocols and approximately matched representation sizes. Performance is assessed using ridge regression, XGBoost, and neural networks, with predictive efficiency measured as R2 relative to encoded representation size per sample. Results show that Bloom Filter encodings remain competitive with classical compressed representations while achieving substantial storage savings. Entropy-based puncturing further reduces representation size with minimal loss in predictive fidelity, yielding improved predictive efficiency. These findings demonstrate that entropy-punctured Bloom Filters provide an effective representation-level compression approach for memory-constrained machine learning.

Problem

• Memory-efficient
• Bloom Filter
• Feature Representation
• Regression Tasks
• Structural Compression

Innovation

• Entropy-Punctured Bloom Filters
• Memory-Efficient Encoding
• Feature Compression
• Predictive Efficiency