International Journal of Innovative Technology and Research
Abstract
Within the recent occasions, several techniques of multi-view hashing were suggested for ingenious similarity search. These techniques mostly rely on spectral, graph otherwise deep learning strategies to achieve data structure protecting encoding. However hashing technique purely along with other schemes is usually responsive to data noise and struggling with high computational difficulty. We recommend a manuscript without supervision multi-view hashing approach, called as Multi-view Alignment Hashing, which fuses several information sources and utilize discriminative low-dimensional embedding by way of nonnegative matrix factorization. Non-negative matrix factorization is a well-liked technique within data mining tasks which seeks to discover a non-negative parts-based representation that gives better visual interpretation of factoring matrices for high-dimensional data
