Dimension-specific search for advanced applications.
Shen, Heng Tao
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Observing that current Global Similarity Measures (GSM) which average the effect of few significant differences on all dimensions may cause pos- sible performance limitation, we propose the first Dimension-specific Similarity Measure (DSM) to take local dimension-specific constraints into consideration. The rationale for DSM is that significant differences on some individual dimen- sions may lead to different semantics. An efficient search algorithm is proposed to achieve fast Dimension-specific KNN (DKNN) retrieval. Experiment results show that our methods outperform traditional methods by large gaps.