Multi-pitch estimation via fast group sparse learning

Kronvall, Ted; Elvander, Filip; Adalbjörnsson, Stefan Ingi; Jakobsson, Andreas

doi:10.1109/eusipco.2016.7760417

Cited by 2 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One note from the simulation results that in the middle figure, the estimated FP rate is consistenly higher than the selected α. This is a result of the dictionary coherence, which makes the signal power in the true support leak into the other variables, as shown in (22). To verify this claim, the bottom figure shows the same estimation scenario, when applied to the noise-only signal, i.e., y = σw, where thus I = ∅, and FPs occur whenever…”

Section: Numerical Resultsmentioning

confidence: 86%

See 1 more Smart Citation

Hyperparameter selection for group-sparse regression: A probabilistic approach

Kronvall

Jakobsson

2018

Signal Processing

Self Cite

View full text Add to dashboard Cite

This work analyzes the effects on support recovery for different choices of the hyper-or regularization parameter in LASSO-like sparse and group-sparse regression problems. The hyperparameter implicitly selects the model order of the solution, and is typically set using cross-validation (CV). This may be computationally prohibitive for large-scale problems, and also often overestimates the model order, as CV optimizes for prediction error rather than support recovery. In this work, we propose a probabilistic approach to select the hyperparameter, by quantifying the type I error (false positive rate) using extreme value analysis. From Monte Carlo simulations, one may draw inference on the upper tail of the distribution of the spurious parameter estimates, and the regularization level may be selected for a specified false positive rate. By solving the scaled group-LASSO problem, the choice of hyperparameter becomes independent of the noise variance. Furthermore, the effects on the false positive rate caused by collinearity in the dictionary is discussed, including ways of circumventing them. The proposed method is compared to other hyperparameter-selection methods in terms of support recovery, false positive rate, false negative rate, and computational complexity. Simulated data illustrate how the proposed method outperforms CV in both computational complexity and support recovery.

show abstract

Section: Numerical Resultsmentioning

confidence: 86%

“…However, by using warm-starts, a solution path may also be calculated quickly using some appropriate implementation of the group-LASSO. Even so, a single point on the solution path must still be selected; often, this is done using cross-validation (CV), as was done, for instance, in [22], for the multi-pitch estimation problem.…”

Section: Introductionmentioning

confidence: 99%