Publications

Preprints

Convergence of alternating minimisation algorithms for dictionary learning
S. Ruetz and K. Schnass
arXiv:2304.01768, 2023. [pdf]
Non-asymptotic bounds for inclusion probabilities in rejective sampling
S. Ruetz and K. Schnass
arXiv:2212.09391, 2022. [pdf]

Journal

Dictionary learning - from local towards global and adaptive
M.C. Pali and K. Schnass
Information and Inference: A Journal of the IMA, 12(3):1295–1346, 2023. [v1pdf] [v2pdf] [toolbox]
Average performance of OMP and Thresholding under dictionary mismatch
M.C. Pali, S. Ruetz and K. Schnass
IEEE Signal Processing Letters, 29:1077–1081, 2022. [pdf]
Submatrices with non-uniformly selected random supports and insights into sparse approximation
S. Ruetz and K. Schnass
SIAM Journal on Matrix Analysis and Applications (SIMAX), 42(3):1268–1289, 2021. [pdf]
Compressed dictionary learning
K. Schnass and F. Teixeira
Journal of Fourier Analysis and Applications 26, Art. Nr. 33, 2020. [pdf] [probox] [toybox]
Online and stable learning of analysis operators
M. Sandbichler and K. Schnass
IEEE Transactions on Signal Processing, 67(1):41–53, 2019. [pdf] [toolbox]
Average performance of Orthogonal Matching Pursuit (OMP) for sparse approximation
K. Schnass
IEEE Signal Processing Letters (arXiv:1809.06684), 25(12):1865–1869, 2018. [pdf]
Fast dictionary learning from incomplete data
V. Naumova and K. Schnass
EURASIP Journal on Advances in Signal Processing, 2018. [pdf] [toolbox]
Convergence radius and sample complexity of ITKM algorithms for dictionary learning
K. Schnass
Applied and Computational Harmonic Analysis, 45(1):22–58, 2018. [pdf] [toolbox]
Local Identification of Overcomplete Dictionaries
K. Schnass
Journal of Machine Learning Research (arXiv:1401.6354), 16(Jun):1211--1242, 2015. [pdf] [toolbox]
On the Identifiability of Overcomplete Dictionaries via the Minimisation Principle Underlying K-SVD
K. Schnass
Applied and Computational Harmonic Analysis, 37(3):464--491, 2014. [pdf]
Learning functions of few arbitrary linear parameters in high dimensions
M. Fornasier, K. Schnass and J. Vybiral
Foundations of Computational Mathematics, 12(2):229--262, 2012. [pdf]
Classification via incoherent subspaces
K. Schnass and P. Vandergheynst
Rejecta Mathematica, 2(1):1--18, 2011. [pdf]
Dictionary identification - sparse matrix-factorisation via l₁-minimisation
R. Gribonval and K. Schnass
IEEE Transactions on Information Theory, 56(7):3523--3539, 2010. [pdf]
Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms
R. Gribonval, H. Rauhut, K. Schnass and P. Vandergheynst
Journal of Fourier Analysis and Applications, 14(5):655--687, 2008. [pdf]
Compressed sensing and redundant dictionaries
H. Rauhut, K. Schnass and P. Vandergheynst
IEEE Transactions on Information Theory, 54(5):2210--2219, 2008. [pdf]
Dictionary preconditioning for greedy algorithms
K. Schnass and P. Vandergheynst
IEEE Transactions on Signal Processing, 56(5):1994--2002, 2008. [pdf]
Average performance analysis for thresholding
K. Schnass and P. Vandergheynst
IEEE Signal Processing Letters, 14(11):828--831, 2007. [pdf]

Conference

A good reason for using OMP: average case results
K. Schnass
SPARS19. [extended abstract]
The adaptive dictionary learning toolbox
C. Rusu and K. Schnass
SPARS19. [extended abstract]
Relaxed contractivity conditions for dictionary learning via Iterative Thresholding and K residual Means
M.C. Pali, K. Schnass and A. Steinicke
SPARS19. [extended abstract]
Sequential learning of analysis operators
M. Sandbichler and K. Schnass
SPARS17. [extended abstract]
Compressed dictionary learning
F. Teixeira and K. Schnass
SPARS17. [extended abstract]
Dictionary learning from incomplete data for efficient image restoration
V. Naumova and K. Schnass
EUSIPCO17. [pdf] [toolbox]
Dictionary identification results for K-SVD with sparsity parameter 1
K. Schnass
SampTA13. [pdf]
Learning functions of few arbitrary linear parameters in high dimensions
M. Fornasier, K. Schnass, and J. Vybiral
SampTA11. [pdf]
Compressed learning of high-dimensional sparse functions
K. Schnass and J. Vybiral
ICASSP11. [pdf]
A union of incoherent spaces model for classification
K. Schnass and P. Vandergheynst
ICASSP10. [pdf]
Basis identification from random sparse samples
R. Gribonval and K. Schnass
SPARS09. [pdf]
Dictionary identifiability from few training samples
R. Gribonval and K. Schnass
EUSIPCO08. [pdf]
Some recovery conditions for basis learning by l_1-minimization
R. Gribonval and K. Schnass
ISCCSP08. [pdf]
Dictionary learning based dimensionality reduction for classification
K. Schnass and P. Vandergheynst
ISCCSP08. [pdf]
Multichannel thresholding with sensing dictionaries
R. Gribonval, B. Mailhe, H. Rauhut, K. Schnass and P. Vandergheynst
CAMSAP07. [pdf]
Average case analysis of multichannel sparse approximations using p- thresholding
R. Gribonval, B. Mailhe, H. Rauhut, K. Schnass and P. Vandergheynst
SPIE Optics and Photonics, Wavelets XII, 2007. [pdf]
Average case analysis of multichannel thresholding
R. Gribonval, B. Mailhe, H. Rauhut, K. Schnass and P. Vandergheynst
ICASSP07. [pdf]

Theses

Dictionary Learning & Related Topics
venia docendi, University of Innsbruck, 2018. [outline]
Sparsity & Dictionaries - Algorithms & Design
PhD Thesis n.4349, Swiss Federal Institute of Technology Lausanne, EPFL, 2009. [pdf]
Gabor Multipliers - A Self-Contained Survey
master's Thesis, University of Vienna, Austria, 2004. [pdf]

Other

A Personal Introduction to Theoretical Dictionary Learning
K. Schnass
Internationale Mathematische Nachrichten (Bulletin of the Austrian Mathematical Society), 228:5--15, 2015. [pdf]

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