Abstract
Over the past few years kernel methods have gained a tremendous
amount of attention as existing linear algorithms can
easily be extended to account for highly non-linear data in a
computationally efficient manner. Unfortunately most kernels
require careful tuning of intrinsic parameters to correctly
model the distribution of the underlying data. For
large-scale problems the multiplicative scaling in time complexity
imposed by introducing free parameters in a crossvalidation
setup will prove computationally infeasible, often
leaving pure ad-hoc estimates as the only option. In this
contribution we investigate a novel randomized approach
for kernel parameter selection in large-scale multi-class data.
We fit a minimum enclosing ball to the class means in Reproducing
Kernel Hilbert Spaces (RKHS), and use the radius
as a quality measure of the space, defined by the kernel
parameter. We apply the developed algorithm to a computer
vision paradigm where the objective is to recognize 72:000
objects among 1:000 classes. Compared to other distance
metrics in the RKHS we find that our randomized approach
provides better results together with a highly competitive
time complexity.
Original language | English |
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Title of host publication | 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |
Publisher | IEEE |
Publication date | 2011 |
ISBN (Print) | 978-1-4577-1621-8 |
ISBN (Electronic) | 978-1-4577-1622-5 |
DOIs | |
Publication status | Published - 2011 |
Event | 2011 IEEE International Workshop on Machine Learning for Signal Processing - Beijing, China Duration: 18 Sep 2011 → 21 Sep 2011 Conference number: 21 https://ieeexplore.ieee.org/xpl/conhome/6058570/proceeding |
Conference
Conference | 2011 IEEE International Workshop on Machine Learning for Signal Processing |
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Number | 21 |
Country/Territory | China |
City | Beijing |
Period | 18/09/2011 → 21/09/2011 |
Internet address |