134 search results - page 1 / 27 » Random Projections for Manifold Learning |
NIPS
2007
13 years 10 months ago
2007
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we show that with a small number M of random projections of sample points in RN belo...
NIPS
2007
13 years 10 months ago
2007
We present a simple variant of the k-d tree which automatically adapts to intrinsic low dimensional structure in data.
AAAI
2008
13 years 11 months ago
2008
Most manifold learning methods consider only one similarity matrix to induce a low-dimensional manifold embedded in data space. In practice, however, we often use multiple sensors...
COMPGEOM
2008
ACM
13 years 10 months ago
2008
ACM
The Johnson-Lindenstrauss random projection lemma gives a simple way to reduce the dimensionality of a set of points while approximately preserving their pairwise distances. The m...
TIP
2010
13 years 3 months ago
2010
The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that capture a single event from a large number of vantage po...