434 search results - page 43 / 87 » Dimensionality Reduction of Clustered Data Sets |
NPL
1998
13 years 8 months ago
1998
Abstract. Incremental learning of sensorimotor transformations in high dimensional spaces is one of the basic prerequisites for the success of autonomous robot devices as well as b...
ICML
2006
IEEE
14 years 9 months ago
2006
IEEE
In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learni...
ICML
2008
IEEE
14 years 9 months ago
2008
IEEE
We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the vector: a cardinality constraint limits the number of...
TNN
2008
13 years 8 months ago
2008
Abstract--High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is ...
SDM
2008
SIAM
13 years 10 months ago
2008
SIAM
In this paper, we propose an efficient and effective method to find arbitrarily oriented subspace clusters by mapping the data space to a parameter space defining the set of possi...