Publications
Preprints / Technical reports
 C. Ye, K. Slavakis, P. V. Patil, S. F. Muldoon, and J. Medaglia. Brainnetwork clustering via kernelARMA modeling and the Grassmannian. arXiv, 2019.
 X. Wang, K. Slavakis, and G. Lerman. Riemannian multimanifold
modeling. arXiv,
2014.
Peerreviewed journals
 G. N. Shetty, K. Slavakis, A. Bose, U. Nakarmi, G. Scutari, and L. Ying. Bilinear modeling of data manifolds for dynamicMRI recovery. IEEE Transactions on Medical Imaging, 2019 (new).
 K. Slavakis. The stochastic Fejérmonotone hybrid
steepest descent method and the hierarchical RLS. IEEE Transactions on Signal Processing, vol. 67, no. 11, pp. 28682883, June 2019.
 K. Slavakis and I. Yamada. Fejérmonotone hybrid steepest
descent method for affinely constrained and composite convex
minimization tasks. Optimization,
vol. 67, no. 11, pp. 19632001, 2018 [arXiv].
 K. Slavakis, S. Salsabilian, D. S. Wack, S. F. Muldoon,
H. E. BaidooWilliams, J. M. Vettel, M. Cieslak and S. T. Grafton. Clustering brainnetwork time series by
Riemannian geometry. IEEE Transactions on Signal and Information Processing over
Networks, vol. 4, no. 3, pp. 519533, Sept. 2018.
 P. A. Traganitis, K. Slavakis and G. B. Giannakis. Sketch and validate for big data
clustering. IEEE
Journal of Selected Topics in Signal Processing, vol. 9, no. 4, pp. 678690, June 2015.
 K. Slavakis, S.J. Kim, G. Mateos and G. B. Giannakis. Stochastic approximation visavis
online learning for big data analytics. IEEE Signal Processing Magazine, vol. 31, no. 6, pp. 124129,
Nov. 2014.
 K. Slavakis, G. B. Giannakis and G. Mateos. Modeling and optimization for big data
analytics. IEEE Signal
Processing Magazine, vol. 31, no. 5, pp. 1831, Sept. 2014.
 K. Slavakis, Y. Kopsinis, S. Theodoridis and S. McLaughlin. Generalized thresholding and
online sparsityaware learning in a union of
subspaces. IEEE
Transactions on Signal Processing, vol. 61, no. 15, pp. 37603773, 2013.
 S. Chouvardas, K. Slavakis, S. Theodoridis and I. Yamada. Stochastic analysis of
hyperslabbased adaptive projected subgradient method under bounded
noise. IEEE Signal
Processing Letters, vol. 20, no. 7, pp. 729732, 2013.
 S. Chouvardas, K. Slavakis and S. Theodoridis. Trading off complexity with communication
costs in distributed adaptive learning via Krylov subspaces for dimensionality
reduction. IEEE Journal
of Selected Topics in Signal Processing, vol. 7, no. 2, pp. 257273, April 2013.
 K. Slavakis and I. Yamada. The adaptive projected subgradient method constrained by families
of quasinonexpansive mappings and its application to online
learning. SIAM Journal on
Optimization, vol. 23, no. 1, pp. 126152, 2013.
 S. Chouvardas, K. Slavakis, Y. Kopsinis and S. Theodoridis. A sparsity promoting adaptive
algorithm for distributed learning. IEEE Transactions on Signal Processing, vol. 60, no. 10, pp. 54125425,
Oct. 2012.
 P. Bouboulis, K. Slavakis and S. Theodoridis. Adaptive learning in complex reproducing kernel
Hilbert spaces employing Wirtinger's
subgradients. IEEE
Transactions on Neural Networks and Learning Systems, vol. 23, no. 3, pp. 425438,
Mar. 2012.
 K. Slavakis, P. Bouboulis and S. Theodoridis. Adaptive multiregression in reproducing kernel
Hilbert spaces: The multiaccess MIMO channel
case. IEEE Transactions
on Neural Networks and Learning Systems, vol. 23, no. 2, pp. 260276, Feb. 2012,
(matlab code).
 S. Chouvardas, K. Slavakis and S. Theodoridis. Adaptive robust distributed learning in
diffusion sensor networks. IEEE Transactions on Signal Processing, vol. 59, no. 10, pp. 46924707,
Oct. 2011.
 Y. Kopsinis, K. Slavakis and S. Theodoridis. Online sparse system identification and signal
reconstruction using projections onto weighted l1
balls. IEEE Transactions
on Signal Processing, vol. 59, no. 3, pp. 936952, March 2011, (matlab code).
 S. Theodoridis, K. Slavakis and I. Yamada. Adaptive learning in a world of projections: A
unifying framework for linear and nonlinear classification and regression
tasks. IEEE Signal
Processing Magazine, vol. 28, no. 1, pp. 97123, January 2011 (2014
IEEE Signal Processing Magazine bestpaper award).
 P. Bouboulis, K. Slavakis and S. Theodoridis. Adaptive kernelbased image denoising employing
semiparametric regularization. IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 14651479, June
2010.
 A. Georgiadis and K. Slavakis. Stability optimization of the coupled oscillator array steady
state solution. IEEE
Transactions on Antennas & Propagation, vol. 58, no. 2, pp. 608612, Feb. 2010.
 M. Yukawa, K. Slavakis and I. Yamada. Multidomain adaptive learning based on feasibility
splitting and adaptive projected subgradient
method. IEICE
Transactions on Fundamentals, E93A (2): 456466, Feb. 2010.
 K. Slavakis, S. Theodoridis and I. Yamada. Adaptive constrained learning in reproducing
kernel Hilbert spaces: The robust beamforming
case. IEEE Transactions on
Signal Processing, 57 (12): 47444764, Dec. 2009,
(matlab code).
 K. Slavakis, S. Theodoridis and I. Yamada. Online kernelbased classification using adaptive
projection algorithms. IEEE Transactions on Signal Processing, 56 (7), Part 1: 27812796, July
2008, (matlab code).
 K. Slavakis and S. Theodoridis. Sliding window generalized kernel affine projection algorithm
using projection mappings, EURASIP Journal on Advances in Signal Processing, Special Issue: Emerging
Machine Learning Techniques in Signal Processing, vol. 2008, 16 pages, 2008.
 A. Georgiadis and K. Slavakis. A convex optimization method for constrained beamsteering in
planar (2D) coupled oscillator antenna
arrays. IEEE Transactions
on Antennas and Propagation, 55 (10): 29252928, October 2007.
 K. Slavakis and I. Yamada. Robust wideband beamforming by the hybrid steepest
descent method. IEEE
Transactions on Signal Processing, 55 (9): 45114522, September 2007.
 M. Yukawa, K. Slavakis and I. Yamada. Adaptive parallel quadraticmetric projection
algorithms. IEEE
Transactions on Audio, Speech, and Signal Processing, 15 (5): 16651680, July 2007.
 K. Slavakis, I. Yamada, and N. Ogura. The adaptive projected subgradient method over the
fixed point set of strongly attracting nonexpansive mappings.
Numerical Functional Analysis
and Optimization, 27 (7&8): 905930, November 2006.
 K. Slavakis, I. Yamada, and K. Sakaniwa. Computation of symmetric positive definite Toeplitz
matrices by the hybrid steepest descent
method. Signal
Processing, 83: 11351140, 2003.
 I. Yamada, K. Slavakis, and K. Yamada. An efficient robust adaptive filtering algorithm based
on parallel subgradient projection techniques. IEEE Transactions on Signal Processing, 50 (5): 10911101, May 2002.
 K. Slavakis and I. Yamada. Biorthogonal unconditional bases of compactly supported matrix
valued wavelets. Numerical
Functional Analysis and Optimization, 22 (1&2): 223253, 2001.
Book Chapters
 K. Slavakis, P. Bouboulis, and S. Theodoridis. Online learning in reproducing kernel Hilbert
spaces. In Academic Press Library in Signal Processing: Volume 1 Signal Processing Theory and
Machine Learning, vol. 1, ch. 17, pp. 883987, Elsevier, 2014.
 S. Theodoridis, Y. Kopsinis, and K. Slavakis. Sparsityaware learning and compressed sensing:
An overview. In Academic Press Library in Signal Processing: Volume 1 Signal Processing Theory
and Machine Learning, vol. 1, ch. 23, pp. 12711377, Elsevier, 2014
[arXiv].
Tutorials/Plenaries
 G. B. Giannakis, K. Slavakis, and G. Mateos. Signal processing tools for big data
analytics. EUSIPCO, Nice: France, Aug. 31  Sept. 4, 2015.
 G. B. Giannakis, K. Slavakis, and G. Mateos. Signal processing tools for big data
analysis. IEEE ICASSP, Brisbane: Australia, April 1920, 2015.
 G. B. Giannakis, K. Slavakis, and G. Mateos. Signal processing for big data. EUSIPCO,
Lisbon: Portugal, Sept. 1, 2014.
 G. B. Giannakis, K. Slavakis, and G. Mateos. Signal processing for big data. IEEE ICASSP,
Florence: Italy, May 5, 2014.
 S. Theodoridis, Y. Kopsinis, K. Slavakis, and S. Chouvardas. Sparsityaware adaptive
learning: A set theoretic estimation approach. IFAC International Workshop on Adaptation and
Learning in Control and Signal Processing (ALCOSP), Caen: France, July 35, 2013.
 S. Theodoridis, I. Yamada, and K. Slavakis. Learning in the context of set theoretic
estimation: An efficient and unifying framework for adaptive machine learning and signal
processing. IEEE ICASSP, Kyoto: Japan, March 2530, 2012, (the slides of parts A and B can be
found
here.)
 S. Theodoridis. Adaptive processing in a world of projections. IEEE MLSP, Cancun: Mexico,
October 1619, 2008 (abstract, slides,) (joint work
with S. Theodoridis and I. Yamada).
Peerreviewed conferences
 K. Slavakis. Stochastic composite convex minimization with affine constraints.
In Proc. of the Asilomar Conference on Signals, Systems and Computers, Pacific Grove,
California, pp. 18711875, Oct. 2831, 2018.
 U. Nakarmi, K. Slavakis, H. Li, C. Zhang, P. Huang, S. Gaire, and L. Ying. MLS:
Selflearned joint manifold geometry and sparsity aware framework for highly accelerated
cardiac cine imaging. In Proc. of the joint annual meeting ISMRMESMRMB, Paris:
France, June 1621, 2018.
 K. Slavakis, A. Konar, and N. Sidiropoulos. Fast projectionbased solvers for the
nonconvex quadratically constrained feasibility problem. In Proc. of the
IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary: Alberta:
Canada, April 1520, 2018.
 U. Nakarmi, K. Slavakis, and L. Ying. MLS: Joint manifoldlearning and sparsityaware
framework for highly accelerated dynamic magnetic resonance imaging. In Proc. of the
IEEE International Symposium on
Biomedical Imaging (ISBI), Washington DC: USA, April 47, 2018.
 K. Slavakis, G. N. Shetty, A. Bose, U. Nakarmi and L. Ying. Bilinear modeling of
manifolddata geometry for dynamicMRI recovery. In Proc. of
the IEEE
International Workshop on Computational Advances in MultiSensor Adaptive Processing
(CAMSAP), Curacao: Dutch Antilles, Dec. 1013, 2017.
 K. Slavakis, S. Salsabilian, D. S. Wack, S. F. Muldoon, H. BaidooWilliams, J. Vettel,
M. Cieslak and S. Grafton. Riemannian multimanifold modeling and
clustering in brain networks. In Proc. of SPIE Optics + Photonics, San Diego: California: USA, 610
Aug., 2017.
 U. Nakarmi, K. Slavakis, J. Lyu, C. Zhang and L. Ying. Beyond lowrank and sparsity:
A manifold driven framework for highly accelerated dynamic magnetic resonance imaging.
In Proc. of the International Society for Magnetic Resonance in Medicine (ISMRM) Meeting,
Honolulu: USA, 2227 April, 2017.
 U. Nakarmi, K. Slavakis, J. Lyu and L. Ying. MMRI: A
manifoldbased framework to highly accelerated dynamic magnetic
resonance imaging. In Proc. of the International
Symposium on Biomedical Imaging (ISBI), Melbourne: Australia,
1821 April, 2017.
 K. Slavakis, I. Yamada and S. Ono. Accelerating the
hybrid steepest descent method for affinely constrained
convex composite minimization tasks. In Proc. of ICASSP,
New Orleans: USA, Mar. 59, 2017.
 K. Slavakis, S. Salsabilian, D. S. Wack, and S. F. Muldoon,
H. BaidooWilliams, J. Vettel, M. Cieslak,
S. Grafton. Clustering brainnetworkconnectivity states
using kernel partial correlations. In Proc. of the 50th
Asilomar Conference on Signals, Systems and Computers,
Pacific Grove, California, Nov. 69, 2016.
 K. Slavakis and I. Yamada. Accelerated hybrid steepest
descent method for solving affinely constrained convex
composite optimization problems. Presented at the
International Conference on Continuous Optimization (ICCOPT),
Tokyo: Japan, Aug. 611, 2016.
 K. Slavakis, S. Salsabilian, D. S. Wack, and
S. F. Muldoon. Clustering timevarying connectivity
networks by Riemannian geometry: The brainnetwork case.
In Proc. of Statistical Signal Processing (SSP), Palma de
Mallorca: Spain, June 2629, 2016.
 U. Nakarmi, Y. Zhou, J. Lyu, K. Slavakis, and L. Ying.
Accelerating dynamic magnetic resonance imaging by nonlinear
sparse coding. In Proc. of ISBI, Prague: Czech Republic,
April 1316, 2016.
 G. V. Karanikolas, G. B. Giannakis, K. Slavakis, and
R. M. Leahy. Multikernel based nonlinear models for
connectivity identification of brain networks. In Proc. of
ICASSP, Shanghai: China, Mar. 2530, 2016.
 P. A. Traganitis, K. Slavakis, and
G. B. Giannakis. Largescale subspace clustering using
random sketching and validation. In Proc. of the Asilomar
Conference on Signals, Systems, and Computers, Nov. 811,
2015.
 X. Wang, K. Slavakis, and G. Lerman. Multimanifold
modeling in nonEuclidean spaces. In Proc. of AISTATS, May
912, San Diego: California: USA, 2015.
 P. A. Traganitis, K. Slavakis, and
G. B. Giannakis. Spectral clustering of largescale
communities via random sketching and validation. Presented at
the Conference on Information Systems and Sciences (CISS),
Baltimore, Maryland, Mar. 1820, 2015.
 P. A. Traganitis, K. Slavakis, and
G. B. Giannakis. Clustering highdimensional data via
random sampling and consensus. Presented at GlobalSIP,
Dec. 35, Atlanta: USA, 2014.
 P. A. Traganitis, K. Slavakis, and G. B. Giannakis. Big
data clustering using random sampling and consensus.
Presented at the Asilomar Conference on Signals, Systems, and
Computers, Nov. 25, 2014.
 K. Slavakis, X. Wang, and G. Lerman. Clustering
highdimensional dynamical systems on lowrank matrix
manifolds. Presented at the Asilomar Conference on
Signals, Systems, and Computers, Nov. 25, 2014.
 M. Mardani, L. Ying, G. Scutari, K. Slavakis, and
G. B. Giannakis. Dynamic MRI using subspace tensor
tracking. In Proc. of the Engineering in Medicine and
Biology Conference (EMBC), Aug. 2630, Chicago, 2014.
 K. Slavakis and G. B. Giannakis. Online dictionary learning
from big data using accelerated stochastic approximation
algorithms. In Proc. ICASSP, Florence: Italy, May 49, 2014
(Special session: "Signal processing for big data").
 M. Zamanighomi, Z. Wang, K. Slavakis, and
G. B. Giannakis. Linear minimum meansquare error estimation based
on highdimensional data with missing values. In Proc. of 48th
Annual Conference on Information Sciences and Systems (CISS),
Princeton University: USA, Mar. 1921, 2014.
 K. Slavakis, Y. Kopsinis, S. Theodoridis. New operators for
fixedpoint theory: The sparsityaware learning case. In Proc. of
EUSIPCO (special session "Advances in set theoretic estimation and
convex analysis for machine learning and signal processing tasks"),
Marrakech: Morocco, Sept. 913, 2013.
 K. Slavakis, Y. Kopsinis, S. Theodoridis, G. B. Giannakis, and
V. Kekatos. Generalized iterative thresholding for sparsityaware
online Volterra system identification. In Proc. of International
Symposium on Wireless Communication Systems (ISWCS), Ilmenau:
Germany, Aug. 2730, 2013.
 S. Theodoridis, Y. Kopsinis, K. Slavakis, and
S. Chouvardas. Sparsityaware adaptive learning: A set theoretic
estimation approach. In Proc. of IFAC International Workshop on
Adaptation and Learning in Control and Signal Processing (ALCOSP),
Caen: France, July 35, 2013, (plenary paper).
 K. Slavakis, G. Leus, and G. B. Giannakis. Online robust portfolio
risk management using total leastsquares and parallel splitting
algorithms. In Proc. of ICASSP, Vancouver: Canada, May
2631, 2013.
 Y. Kopsinis, K. Slavakis, S. Theodoridis, and
S. McLaughlin. Thresholdingbased sparsitypromoting online algorithms
of low complexity. In Proc. of ISCAS, Beijing, China, May 1923,
2013.
 K. Slavakis, G. B. Giannakis, and G. Leus. Robust sparse embedding
and reconstruction via dictionary learning. In Proc. of 47th Annual
Conference on Information Sciences and Systems (CISS), Johns Hopkins
University: Baltimore: USA, Mar. 2022, 2013.
 S. Chouvardas, K. Slavakis, Y. Kopsinis, and
S. Theodoridis. Sparsitypromoting adaptive algorithm for distributed
learning in diffusion networks. In Proceedings of the European Signal
Processing Conference (EUSIPCO), Bucharest: Romania, Aug. 2731,
2012.
 Y. Kopsinis, K. Slavakis, S. Theodoridis, and
S. McLaughlin. Generalized thresholding sparsityaware algorithm
for low complexity online learning. In Proceedings of the IEEE
International Conference on Acoustics, Speech, and Signal Processing
(ICASSP), pp. 32773280, Kyoto: Japan, March 2530,
2012.
 K. Slavakis, Y. Kopsinis, and S. Theodoridis. Robust adaptive
sparse system identification by using weighted l1 balls and Moreau
envelopes. In Proceedings of the European Signal
Processing Conference (EUSIPCO), Barcelona: Spain, Aug. 29 
Sept. 2, 2011, (presented in the Special Session "Sparsity
aware processing: theory and applications").
 Y. Kopsinis, K. Slavakis, S. Theodoridis, and
S. McLaughlin. Reduced complexity online sparse signal
reconstruction using projections onto weighted l1 balls. In
Proceedings of the International Conference on Digital Signal
Processing (DSP), Special Session "Sparsityaware signal
processing", Corfu: Greece, July 68,
2011, (Invited).
 K. Slavakis, Y. Kopsinis, and S. Theodoridis. Revisiting
adaptive leastsquares estimation and application to online sparse
signal recovery. In Proceedings of the IEEE International Conference on
Acoustics, Speech, and Signal Processing (ICASSP), pp. 42924295,
Prague: Czech Republic, May 2227,
2011.
 S. Chouvardas, K. Slavakis, and S. Theodoridis. Trading off
communications bandwidth with accuracy in adaptive diffusion
networks. In Proceedings of the IEEE International Conference on
Acoustics, Speech, and Signal Processing (ICASSP), pp. 20482051,
Prague: Czech Republic, May 2227, 2011.
 K. Slavakis, S. Theodoridis, and I. Yamada. Low complexity
projectionbased adaptive algorithm for sparse system identification
and signal reconstruction. In Proceedings of the Asilomar
Conference on Signals, Systems, and Computers, Pacific Grove:
California: USA, November 710,
2010, (Invited).
 P. Bouboulis, K. Slavakis, and S. Theodoridis. Edge preserving
image denoising in reproducing kernel Hilbert spaces. In
Proceedings of the IAPR International Conference on Pattern
Recognition (ICPR), pp. 26602663, Istanbul: Turkey, August 2326,
2010 (bestpaper award, track III: Signal,
speech, image and video processing).
 S. Chouvardas, K. Slavakis, and S. Theodoridis. A novel
adaptive algorithm for diffusion networks using projections onto
hyperslabs. In Proceedings of the IAPR Workshop on Cognitive
Information Processing (CIP), pp. 393398, Italy, June 1416,
2010 (beststudentpaper award).
 K. Slavakis, Y. Kopsinis, and S. Theodoridis. Adaptive
algorithm for sparse system identification using projections onto
weighted l1 balls. In Proceedings of the IEEE International
Conference on Acoustics, Speech, and Signal Processing (ICASSP),
pp. 37423745, Dallas: Texas: USA, March 1419, 2010.
 M. Yukawa, K. Slavakis, and I. Yamada. Multidomain adaptive
filtering by feasibility splitting. In Proceedings of the IEEE
International Conference on Acoustics, Speech, and Signal Processing
(ICASSP), Dallas: Texas: USA, March 1419, 2010.
 M. Yukawa, K. Slavakis, and I. Yamada. Signal processing in
dual domain by adaptive projected subgradient method. In
Proceedings of the International Conference on Digital Signal
Processing (DSP), Santorini: Greece, July 57, 2009.
 K. Slavakis, P. Bouboulis, and S. Theodoridis. Online kernel
receiver for multiaccess MIMO channels. In Proceedings of the IEEE
International Workshop on Signal Processing Advances in Wireless
Communications (SPAWC), pp. 221224, Perugia: Italy, June 2124,
2009.
 K. Slavakis and S. Theodoridis. Affinely constrained online
learning and its application to beamforming. In Proceedings of the
IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP), pp. 15731576, Taipei, April 1924, 2009.
 K. Slavakis, S. Theodoridis, and I. Yamada. Constrained
adaptive learning in reproducing kernel Hilbert spaces: the
beamforming paradigm. In Proceedings of the IEEE Machine Learning
for Signal Processing (MLSP) Workshop, pp. 3237, Cancun: Mexico,
October 1619, 2008.
 K. Slavakis, S. Theodoridis, and I. Yamada. Robust adaptive
nonlinear beamforming by kernels and projection mappings. In
Proceedings of EUSIPCO, Lausanne: Switzerland, August 2529,
2008.
 K. Slavakis and S. Theodoridis. Optimal sliding window
sparsification for online kernelbased classification by
projections. In Proceedings of the IAPR Cognitive Information
Processing (CIP) Workshop, Santorini: Greece, pp. 3035, June 2008.
 K. Slavakis and S. Theodoridis. Sliding window online
kernelbased classification by projection mappings. In Proceedings
of the IEEE ISCAS, Seattle: USA, pp. 4952, May 2008.
 F. FourliKartsouni, K. Slavakis, G. Kouroupetroglou, and
S. Theodoridis. A Bayesian network approach to semantic labelling of
text formatting in XML corpora of documents. Lecture Notes in
Computer Science (LNCS), Vol. 4556, pp. 299308, 2007.
 K. Slavakis, S. Theodoridis, and I. Yamada. Online sparse
kernelbased classification by projections. In Proceedings of the
IEEE Machine Learning for Signal Processing (MLSP), Thessaloniki:
Greece, pp. 294299, August 2007.
 K. Slavakis, S. Theodoridis, and I. Yamada. Online kernelbased
classification by projections. In Proceedings of the IEEE ICASSP,
Hawaii: USA, vol. II, pp. 425428, April 2007.
 I. Yamada, K. Slavakis, M. Yukawa, and R. Cavalcante. The
adaptive projected subgradient method and its applications to signal
processing problems. In Proceedings of the IEEE ISCAS (Invited),
Kos: Greece, May 2006.
 K. Slavakis, M. Yukawa, and I. Yamada. Robust Capon beamforming
by the adaptive projected subgradient method. In Proceedings of
the IEEE ICASSP, Toulouse: France, pp. 10051008, May 2006.
 M. Yukawa, K. Slavakis, and I. Yamada. Stereo echo canceler by
adaptive projected subgradient method with multiple roomacoustics
information. In Proceedings of the IWAENC, S0315, pp. 185188,
Eindhoven: The Netherlands, September 2005.
 K. Slavakis, I. Yamada, N. Ogura, and M. Yukawa. Adaptive
projected subgradient method and set theoretic adaptive filtering with
multiple convex constraints. In Proceedings of the 38th Asilomar
Conference on Signals, Systems, and Computers, November 2004.
 K. Slavakis, I. Yamada, and K. Sakaniwa. Spectrum estimation of
real vector wide sense stationary processes by the hybrid steepest
descent method. In Proceedings of the IEEE ICASSP, Orlando: USA,
May 2002.
 I. Yamada, K. Slavakis, and K. Yamada. An efficient robust
adaptive filtering scheme based on parallel subgradient projection
techniques. In Proceedings of the IEEE ICASSP, Salt Lake City:
USA, May 2001.
 K. Slavakis and I. Yamada. Compactly supported matrix valued
waveletsBiorthogonal unconditional bases. In Proceedings of the
IEEE ISCAS (Invited: Special Session), Sydney: Australia, May 2001.
 K. Slavakis and I. Yamada. Biorthogonal bases of compactly
supported matrix valued wavelets. In Proceedings of the IEEE
ISSPA, volume 2, pp. 981984, Brisbane: Australia, August 1999.
Workshops
 K. Slavakis, G. B. Giannakis, and G. Leus. Nonlinear embedding and
reconstruction via locally affine dictionary learning. Presented at
the Information Theory and Applications (ITA) Workshop, San Diego: USA,
Feb. 1015, 2013.
