69 search results - page 4 / 14 » Learning with Distance Substitution Kernels |
ICML
2004
IEEE
15 years 2 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 ...
ICML
2003
IEEE
15 years 10 months ago
2003
IEEE
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, we...
117
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DAGM
2011
Springer
13 years 9 months ago
2011
Springer
Many unsupervised learning algorithms make use of kernels that rely on the Euclidean distance between two samples. However, the Euclidean distance is optimal for Gaussian distribut...
92
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PAMI
2006
14 years 9 months ago
2006
High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suffers from ...
ICPR
2010
IEEE
15 years 4 months ago
2010
IEEE
This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original dist...