Mathematical Models for Local Sensing Hashes
This work addresses the curse of dimensionality in data searches for applications like clustering and outlier detection, but it appears incremental as it focuses on modeling rather than introducing a new method.
The paper tackles the problem of time-consuming neighbor searches in high-dimensional data by proposing mathematical models for local sensing hashes, an approximated index structure, to accelerate clustering and outlier detection with low error rates.
As data volumes continue to grow, searches in data are becoming increasingly time-consuming. Classical index structures for neighbor search are no longer sustainable due to the "curse of dimensionality". Instead, approximated index structures offer a good opportunity to significantly accelerate the neighbor search for clustering and outlier detection and to have the lowest possible error rate in the results of the algorithms. Local sensing hashes is one of those. We indicate directions to mathematically model the properties of it.