Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the train...
This paper addresses the topic of how architectural visual experience can be represented and utilised by a software system. The long-term aim is to equip an artificial agent with ...
Stephan K. Chalup, Riley Clement, Chris Tucker, Mi...
We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-ba...
In this paper we give approximation algorithms for several proximity problems in high dimensional spaces. In particular, we give the rst Las Vegas data structure for (1 + )-neares...
We provide evidence that non-linear dimensionality reduction, clustering and data set parameterization can be solved within one and the same framework. The main idea is to define ...