Infoscience

Report

Deliverable 3.1: Report on Learning Structured Sparse Models

The aim of this work package (WP) is to explore approaches to learn structured sparse models, that is sparse models where the sparsity assumption seems not to be sufficient, or when there is hope to exploit some additional knowledge together with the sparsity hypothesis. The concept of structured sparsity can denote in fact various types of structure, which are strongly different in nature, yet related and interacting.

    Keywords: LTS2 ; structure sparse models ; learning

    Note:

    Deliverable 3.1 of the Sparse Models, Algorithms and Learning for Large-Scale Data (SMALL) project founded by the EU FP7 FET-Open program.

    Reference

    • EPFL-REPORT-166977

    Record created on 2011-06-18, modified on 2016-08-09

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