Structured metric learning for high dimensional problems
JV Davis, IS Dhillon - Proceedings of the 14th ACM SIGKDD …, 2008 - dl.acm.org
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 …
depend on the assumption that the underlying distance functions reflect domain-specific …
[PDF][PDF] Structured Metric Learning for High Dimensional Problems
JV Davis, IS Dhillon - 2008 - Citeseer
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 …
depend on the assumption that the underlying distance functions reflect domain-specific …
[PDF][PDF] Structured Metric Learning for High Dimensional Problems
JV Davis, IS Dhillon - 2008 - cs.utexas.edu
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 …
depend on the assumption that the underlying distance functions reflect domain-specific …
[PDF][PDF] Structured Metric Learning for High Dimensional Problems
JV Davis, IS Dhillon - 2008 - scholar.archive.org
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 …
depend on the assumption that the underlying distance functions reflect domain-specific …
[PDF][PDF] Structured Metric Learning for High Dimensional Problems
JV Davis, IS Dhillon - 2008 - scholar.archive.org
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 …
depend on the assumption that the underlying distance functions reflect domain-specific …
[PDF][PDF] Structured Metric Learning for High Dimensional Problems
JV Davis, IS Dhillon - 2008 - Citeseer
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 …
depend on the assumption that the underlying distance functions reflect domain-specific …
[PDF][PDF] Structured Metric Learning for High Dimensional Problems
JV Davis, IS Dhillon - 2008 - cs.utexas.edu
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 …
depend on the assumption that the underlying distance functions reflect domain-specific …