Introduction -- Preliminaries -- Sparsity-Constrained Optimization -- Background -- 1-bit Compressed Sensing -- Estimation Under Model-Based Sparsity -- Projected Gradient Descent for lp-constrained Least Squares -- Conclusion and Future Work

Summary

This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a"greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models

Notes

Ph. D. Carnegie Mellon University (2013?)

Bibliography

Includes bibliographical references

Notes

Online resource; title from PDF title page (SpringerLink, viewed October 14, 2013)