73 search results - page 7 / 15 » Sparse Unsupervised Dimensionality Reduction Algorithms |
AUSAI
2007
Springer
14 years 1 months ago
2007
Springer
Dimensional reduction may be effective in order to compress data without loss of essential information. Also, it may be useful in order to smooth data and reduce random noise. The...
ICPR
2002
IEEE
14 years 8 months ago
2002
IEEE
This paper considers a recently proposed method for unsupervised learning and dimensionality reduction, locally linear embedding (LLE). LLE computes a compact representation of hi...
ICASSP
2011
IEEE
12 years 11 months ago
2011
IEEE
A K∗ -sparse vector x∗ ∈ RN produces measurements via linear dimensionality reduction as u = Φx∗ + n, where Φ ∈ RM×N (M < N), and n ∈ RM consists of independent ...
ISDA
2009
IEEE
14 years 2 months ago
2009
IEEE
For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors t...
NIPS
2001
13 years 9 months ago
2001
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...