AI Chat AI Image Generator AI Video Text to Speech

Sparsity-accuracy trade-off in MKL

01/15/2010

∙

by Ryota Tomioka, et al.

∙

∙

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.

Ryota Tomioka
32 publications
Taiji Suzuki
84 publications

page 1

page 2

page 3

page 4

research

∙ 03/02/2012

Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness

We investigate the learning rate of multiple kernel learning (MKL) with ...

0 Taiji Suzuki, et al. ∙

research

∙ 11/13/2010

Regularization Strategies and Empirical Bayesian Learning for MKL

Multiple kernel learning (MKL), structured sparsity, and multi-task lear...

0 Ryota Tomioka, et al. ∙

research

∙ 03/02/2011

Fast Convergence Rate of Multiple Kernel Learning with Elastic-net Regularization

We investigate the learning rate of multiple kernel leaning (MKL) with e...

0 Taiji Suzuki, et al. ∙

research

∙ 03/27/2011

Sharp Convergence Rate and Support Consistency of Multiple Kernel Learning with Sparse and Dense Regularization

We theoretically investigate the convergence rate and support consistenc...

0 Taiji Suzuki, et al. ∙

research

∙ 09/07/2018

Sparse Kernel PCA for Outlier Detection

In this paper, we propose a new method to perform Sparse Kernel Principa...

0 Rudrajit Das, et al. ∙

research

∙ 09/28/2009

SpicyMKL

We propose a new optimization algorithm for Multiple Kernel Learning (MK...

0 Taiji Suzuki, et al. ∙

research

∙ 04/17/2019

DeepNovoV2: Better de novo peptide sequencing with deep learning

We introduce DeepNovoV2, the state-of-the-art neural networks based mode...

0 Rui Qiao, et al. ∙