Convex relaxations of penalties for sparse correlated variables with bounded total variation

Author:

Belilovsky Eugene,Argyriou Andreas,Varoquaux Gaël,Blaschko Matthew

Funder

DIGITEO

European Commission

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference31 articles.

1. Argyriou, A., Foygel, R., & Srebro, N. (2012). Sparse prediction with the $$k$$ k -support norm. In F. Pereira, C.J.C. Burges, L. Bottou, & K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25 (pp. 1457–1465). Curran Associates, Inc.

2. Bach, F., Jenatton, R., Mairal, J., & Obozinski, G. (2012). Optimization with sparsity-inducing penalties. Foundations and Trends in Machine Learning, 4(1), 1–106.

3. Backus, A., Jensen, O., Meeuwissen, E., van Gerven, M., & Dumoulin, S. (2011). Investigating the temporal dynamics of long term memory representation retrieval using multivariate pattern analyses on magnetoencephalography data. Tech. rep.

4. Baldassarre, L., Morales, J., Argyriou, A., & Pontil, M. (2012a). A general framework for structured sparsity via proximal optimization. In AISTATS, pp. 82–90.

5. Baldassarre, L., Mourao-Miranda, J., & Pontil, M. (2012b). Structured sparsity models for brain decoding from fMRI data. In PRNI.

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