Data structures for similarity search are commonly evaluated on data in vector spaces, but distance-based data structures are also applicable to non-vector spaces with no natural concept of dimensionality. The intrinsic dimensionality statistic of Chávez and Navarro provides a way to compare the performance of similarity indexing and search algorithms across different spaces, and predict the performance of index data structures on non-vector spaces by relating them to equivalent vector spaces. We characterise its asymptotic behaviour, and give experimental results to calibrate these comparisons. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Skala, M. (2005). Measuring the difficulty of distance-based indexing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3772 LNCS, pp. 103–114). https://doi.org/10.1007/11575832_12
Mendeley helps you to discover research relevant for your work.