38 search results - page 1 / 8 » Near Optimal Dimensionality Reductions That Preserve Volumes |
APPROX
2008
Springer
13 years 7 months ago
2008
Springer
ICML
2004
IEEE
13 years 10 months ago
2004
IEEE
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
WEBI
2010
Springer
13 years 2 months ago
2010
Springer
High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dim...
ISCA
2005
IEEE
13 years 10 months ago
2005
IEEE
Minimizing latency and maximizing throughput are important goals in the design of routing algorithms for interconnection networks. Ideally, we would like a routing algorithm to (a...
NIPS
2008
13 years 6 months ago
2008
This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data points. We argue that constraining the mapping between the high a...