38 search results - page 1 / 8 » Near Optimal Dimensionality Reductions That Preserve Volumes |
97
Voted
APPROX
2008
Springer
15 years 5 months ago
2008
Springer
146
Voted
ICML
2004
IEEE
15 years 9 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 ...
160
Voted
WEBI
2010
Springer
15 years 1 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...
149
Voted
ISCA
2005
IEEE
15 years 9 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...
132
Voted
NIPS
2008
15 years 5 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...