1 We consider the problem of similarity search in applications where the cost of computing the similarity between two records is very expensive, and the similarity measure is not a...
Chris Jermaine, Fei Xu, Mingxi Wu, Ravi Jampani, T...
Correlation is one of the most widely used similarity measures in machine learning like Euclidean and Mahalanobis distances. However, compared with proposed numerous discriminant ...
Heterogeneous information networks that contain multiple types of objects and links are ubiquitous in the real world, such as bibliographic networks, cyber-physical networks, and ...
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
ibe an abstract data model of protein structures by representing the geometry of proteins using spatial data types and present a framework for fast structural similarity search bas...