CRH: A Simple Benchmark Approach to Continuous Hashing
This addresses continuous hashing for streaming data like media, but appears incremental as it builds on existing adaptive coding methods.
The paper tackles the problem of encoding sequential data streams with continuous hashing by proposing CRH (continuous random hashing), which uses random selection to adaptively approximate differential encoding patterns without iteration. Experimental results show it provides outstanding performance as a benchmark approach.
In recent years, the distinctive advancement of handling huge data promotes the evolution of ubiquitous computing and analysis technologies. With the constantly upward system burden and computational complexity, adaptive coding has been a fascinating topic for pattern analysis, with outstanding performance. In this work, a continuous hashing method, termed continuous random hashing (CRH), is proposed to encode sequential data stream, while ignorance of previously hashing knowledge is possible. Instead, a random selection idea is adopted to adaptively approximate the differential encoding patterns of data stream, e.g., streaming media, and iteration is avoided for stepwise learning. Experimental results demonstrate our method is able to provide outstanding performance, as a benchmark approach to continuous hashing.