Sparsity-accuracy trade-off in MKL

2010/01/15 by Ryota Tomioka, Taiji Suzuki, Tomioka, Ryota +1
Computer Science · Engineering · #Applications (stat.AP) #FOS: Computer and information sciences #Face and Expression Recognition #Image and Signal Denoising Methods #Machine Learning (stat.ML) #Methodology (stat.ME) #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1001.2615

openalex publication_date 2010/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• L2 Regularization for Learning Kernels
• Consistency of the group Lasso and multiple kernel learning

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