Skip to yearly menu bar Skip to main content


Poster

An Iterative Min-Min Optimization Method for Sparse Bayesian Learning

Yasen Wang · Junlin Li · Zuogong Yue · Ye Yuan

Hall C 4-9 #1601
[ ] [ Paper PDF ]
[ Poster
Tue 23 Jul 2:30 a.m. PDT — 4 a.m. PDT

Abstract:

As a well-known machine learning algorithm, sparse Bayesian learning (SBL) can find sparse representations in linearly probabilistic models by imposing a sparsity-promoting prior on model coefficients. However, classical SBL algorithms lack the essential theoretical guarantees of global convergence. To address this issue, we propose an iterative Min-Min optimization method to solve the marginal likelihood function (MLF) of SBL based on the concave-convex procedure. The method can optimize the hyperparameters related to both the prior and noise level analytically at each iteration by re-expressing MLF using auxiliary functions. Particularly, we demonstrate that the method globally converges to a local minimum or saddle point of MLF. With rigorous theoretical guarantees, the proposed novel SBL algorithm outperforms classical ones in finding sparse representations on simulation and real-world examples, ranging from sparse signal recovery to system identification and kernel regression.

Chat is not available.