576 search results - page 38 / 116 » Structured metric learning for high dimensional problems |
155
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ISNN
2011
Springer
14 years 19 days ago
2011
Springer
This paper presents a new method that uses orthogonalized features for time series clustering and classification. To cluster or classify time series data, either original data or...
ICANN
2009
Springer
15 years 2 months ago
2009
Springer
Learning to solve small instances of a problem should help in solving large instances. Unfortunately, most neural network architectures do not exhibit this form of scalability. Our...
101
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ECCV
2010
Springer
15 years 4 days ago
2010
Springer
Confronted with the high-dimensional tensor-like visual data, we derive a method for the decomposition of an observed tensor into a low-dimensional structure plus unbounded but spa...
ICML
2010
IEEE
14 years 10 months ago
2010
IEEE
Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
ALT
2004
Springer
15 years 6 months ago
2004
Springer
Kernel functions are typically viewed as providing an implicit mapping of points into a high-dimensional space, with the ability to gain much of the power of that space without inc...