Joint Screening Tests for Lasso

This paper focusses on “safe” screening techniques for the LASSO problem. Motivated by the need for low-complexity algorithms, we propose a new approach, dubbed “joint screening test”, allowing to screen a set of atoms by carrying out one single test. The approach is particularized to two different sets of atoms, respectively expressed as sphere and dome regions. After presenting the mathematical derivations of the tests, we elaborate on their relative effectiveness and discuss the practical use of such procedures.

[1]  Rémi Gribonval,et al.  Dynamic Screening with Approximate Dictionaries , 2017 .

[2]  Cédric Herzet,et al.  Safe screening tests for LASSO based on firmly non-expansiveness , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Mário A. T. Figueiredo Teaching a new trick to an old dog: Revisiting the quadratic programming formulation of sparse recovery using ADMM , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  Peter J. Ramadge,et al.  Screening Tests for Lasso Problems , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Jie Wang,et al.  Lasso screening rules via dual polytope projection , 2012, J. Mach. Learn. Res..

[6]  Kristiaan Pelckmans,et al.  An ellipsoid based, two-stage screening test for BPDN , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[7]  Hao Xu,et al.  Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries , 2011, NIPS.

[8]  Gabriel Peyré,et al.  Exact Support Recovery for Sparse Spikes Deconvolution , 2013, Foundations of Computational Mathematics.

[9]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[10]  Alexandre Gramfort,et al.  Mind the duality gap: safer rules for the Lasso , 2015, ICML.

[11]  Rémi Gribonval,et al.  Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso , 2014, IEEE Transactions on Signal Processing.

[12]  Peter J. Ramadge,et al.  Fast lasso screening tests based on correlations , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[14]  Laurent El Ghaoui,et al.  Safe Feature Elimination in Sparse Supervised Learning , 2010, ArXiv.

[15]  M. R. Osborne,et al.  A new approach to variable selection in least squares problems , 2000 .

[16]  Emmanuel J. Candès,et al.  Towards a Mathematical Theory of Super‐resolution , 2012, ArXiv.

[17]  Peter Kulchyski and , 2015 .

[18]  PeyréGabriel,et al.  Exact Support Recovery for Sparse Spikes Deconvolution , 2015 .