  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.
