Engineering a Simplified 0-Bit Consistent Weighted Sampling

The Min-Hashing approach to sketching has become an important tool in data analysis, search, and classi cation. To apply it to real-valued datasets, the ICWS algorithm has become a seminal approach that is widely used, and provides state-of-the-art performance for this problem space. However, ICWS su ers a computational burden as the sketch size K increases. We develop a new Simpli ed approach to the ICWS algorithm, that enables us to obtain over 20x speedups compared to the standard algorithm. The veracity of our approach is demonstrated empirically on multiple datasets, showing that our new Simpli ed CWS obtains the same quality of results while being an order of magnitude faster.