Sparsity-accuracy trade-off in MKL

Details Sparsity-accuracy trade-off in MKL Sparsity-accuracy trade-off in MKL

2010-01-15

arxiv.org/abs/1001.2615v1

Statistics

Machine Learning

8pages, 2 figures

Scientific paper

We empirically investigate the best trade-off between sparse and
uniformly-weighted multiple kernel learning (MKL) using the elastic-net
regularization on real and simulated datasets. We find that the best trade-off
parameter depends not only on the sparsity of the true kernel-weight spectrum
but also on the linear dependence among kernels and the number of samples.

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