Structured matrices in compressed sensing
| advisor: | doc. RNDr. Jan Vybíral, Ph.D. |
| e-mail: | show e-mail |
| type: | bachelor thesis, master thesis |
| branch of study: | MI_AMSM, MINF |
| key words: | Random matrices, compressed sensing, Johnson-Lindenstrauss Lemma |
| description: | The student will review some basic properties of random matrices in the area of compressed sensing. Then (s)he will discuss the role of structured random matrices - the speed up of matrix-vector multiplication, the reduced amount of random bits needed, theoretical guarantees for their performance, real-life performance for some specific problems. |
| references: | H. Boche, R. Calderbank, and G. Kutyniok, A Survey of Compressed Sensing, First chapter in Compressed Sensing and its Applications, Birkhäuser, Springer, 2015 F. Krahmer and R. Ward, New and improved Johnson–Lindenstrauss embeddings via the restricted isometry property, SIAM Journal on Mathematical Analysis, 2011 |
| note: | The preferred language of the thesis is English. |
| last update: | 14.07.2022 09:20:08 |
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