We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
For memory constrained environments like embedded systems, optimization for program size is often as important, if not more important, as optimization for execution speed. Commonl...
This talk outlines new facilities within human media spaces supporting embodied interaction between humans, animals, and computation both socially and physically, with the aim of ...
Spatial classification is the task of learning models to predict class labels based on the features of entities as well as the spatial relationships to other entities and their fe...
Extracting dense sub-components from graphs efficiently is an important objective in a wide range of application domains ranging from social network analysis to biological network...
Nan Wang, Srinivasan Parthasarathy, Kian-Lee Tan, ...