Publications
New preprints and technical reports
...
Peer-reviewed journals
- K. Slavakis, G. N. Shetty, L. Cannelli, G. Scutari, U. Nakarmi, and L. Ying. Kernel regression imputation in manifolds via bi-linear modeling: The dynamic-MRI case. IEEE Transactions on Computational Imaging, vol. 8, pp. 133-147, 2022. (new)
[Julia code for the numerical tests: GitHub.]
- C. Ye, K. Slavakis, J. Nakuci, S. F. Muldoon, and J. Medaglia. Fast
sequential clustering in Riemannian manifolds for dynamic and
time-series-annotated multilayer networks. IEEE Open Journal of Signal Processing, vol. 2, pp. 67-84, 2021.
[Matlab code for the eGCT/fastGCT algorithms: GitHub.]
- C. Ye, K. Slavakis, P. V. Patil, J. Nakuci, S. F. Muldoon, and J. Medaglia. Network clustering via kernel-ARMA modeling and the Grassmannian: The brain-network case. Signal Processing, vol. 179, pp. 107834, February 2021.
- G. N. Shetty, K. Slavakis, A. Bose, U. Nakarmi, G. Scutari, and
L. Ying. Bi-linear modeling of data manifolds for dynamic-MRI
recovery. IEEE Transactions on Medical Imaging, vol. 39,
no. 3, pp. 688-702, March 2020.
- K. Slavakis and S. Banerjee. Robust hierarchical-optimization
RLS against sparse outliers. IEEE Signal Processing Letters, vol. 27,
pp. 171-175, 2020.
[Julia code for the numerical tests: GitHub.]
- K. Slavakis. The stochastic Fejér-monotone hybrid
steepest descent method and the hierarchical RLS. IEEE Transactions on Signal Processing, vol. 67, no. 11, pp. 2868-2883, June 2019.
[Matlab code for the numerical tests: GitHub.]
- K. Slavakis and I. Yamada. Fejér-monotone hybrid steepest
descent method for affinely constrained and composite convex
minimization tasks. Optimization, vol. 67, no. 11, pp. 1963-2001,
2018. [arXiv]
- K. Slavakis, S. Salsabilian, D. S. Wack, S. F. Muldoon,
H. E. Baidoo-Williams, J. M. Vettel, M. Cieslak and
S. T. Grafton. Clustering brain-network time series by
Riemannian geometry. IEEE Transactions on Signal and Information
Processing over Networks, vol. 4, no. 3, pp. 519-533,
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. 678-690, June 2015.
- K. Slavakis, S.-J. Kim, G. Mateos and G. B. Giannakis. Stochastic
approximation vis-a-vis online learning for big data analytics. IEEE Signal
Processing Magazine, vol. 31, no. 6, pp. 124-129, 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. 18-31, Sept. 2014.
- K. Slavakis, Y. Kopsinis, S. Theodoridis and S. McLaughlin. Generalized
thresholding and online sparsity-aware learning in a union of subspaces.
IEEE
Transactions on Signal Processing, vol. 61, no. 15, pp. 3760-3773,
2013.
- S. Chouvardas, K. Slavakis, S. Theodoridis and I. Yamada. Stochastic
analysis of hyperslab-based adaptive projected subgradient method under
bounded noise. IEEE Signal Processing Letters, vol. 20, no. 7,
pp. 729-732, 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. 257-273, April 2013.
- K. Slavakis and I. Yamada. The adaptive projected subgradient method
constrained by families of quasi-nonexpansive mappings and its application
to online learning. SIAM Journal on Optimization, vol. 23, no. 1,
pp. 126-152, 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. 5412-5425,
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. 425-438, 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. 260-276, Feb. 2012.
- 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. 4692-4707,
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. 936-952, March 2011.
- 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. 97-123, January 2011 (2014 IEEE Signal Processing
Magazine best-paper award).
- P. Bouboulis, K. Slavakis and S. Theodoridis. Adaptive kernel-based image
denoising employing semi-parametric regularization. IEEE
Transactions on Image Processing, vol. 19, no. 6, pp. 1465-1479, 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. 608-612,
Feb. 2010.
- M. Yukawa, K. Slavakis and I. Yamada. Multi-domain adaptive learning
based on feasibility splitting and adaptive projected subgradient
method. IEICE Transactions on Fundamentals, E93-A (2): 456-466,
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): 4744-4764, Dec. 2009.
- K. Slavakis, S. Theodoridis and I. Yamada. Online kernel-based
classification using adaptive projection algorithms. IEEE
Transactions on Signal Processing, 56 (7), Part 1: 2781-2796, July 2008.
- 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 beam-steering in planar (2-D) coupled oscillator antenna
arrays. IEEE Transactions on Antennas and Propagation, 55 (10):
2925-2928, October 2007.
- K. Slavakis and I. Yamada. Robust wideband beamforming by the hybrid
steepest descent method. IEEE
Transactions on Signal Processing, 55 (9): 4511-4522, September
2007.
- M. Yukawa, K. Slavakis and I. Yamada. Adaptive parallel quadratic-metric
projection algorithms. IEEE
Transactions on Audio, Speech, and Signal Processing, 15 (5): 1665-1680,
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): 905-930, 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: 1135-1140, 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): 1091-1101, May 2002.
- K. Slavakis and I. Yamada. Biorthogonal unconditional bases of compactly
supported matrix valued wavelets. Numerical
Functional Analysis and Optimization, 22 (1&2): 223-253, 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. 883-987,
Elsevier, 2014.
- S. Theodoridis, Y. Kopsinis, and K. Slavakis. Sparsity-aware 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. 1271-1377,
Elsevier, 2014. [arXiv]
Tutorials/Plenaries
- [Tutorial] K. Slavakis. Learning from data in manifolds: Methods, applications, and recent
developments. Signal Processing (SIP) Symposium, Tokyo Institute of Technology: Tokyo: Japan, November 9-12, 2021. (new)
- [Tutorial] G. B. Giannakis, K. Slavakis, and G. Mateos. Signal processing tools for
big data analytics. EUSIPCO, Nice: France, Aug. 31 - Sept. 4, 2015.
- [Tutorial] G. B. Giannakis, K. Slavakis, and G. Mateos. Signal processing tools for
big data analysis. IEEE ICASSP, Brisbane: Australia, April 19-20, 2015.
- [Tutorial] G. B. Giannakis, K. Slavakis, and G. Mateos. Signal processing for big
data. EUSIPCO, Lisbon: Portugal, Sept. 1, 2014.
- [Tutorial] G. B. Giannakis, K. Slavakis, and G. Mateos. Signal processing for big
data. IEEE ICASSP, Florence: Italy, May 5, 2014.
- [Tutorial] S. Theodoridis, Y. Kopsinis, K. Slavakis, and S. Chouvardas. Sparsity-aware adaptive learning: A set theoretic estimation
approach. IFAC International Workshop on Adaptation and Learning in
Control and Signal Processing (ALCOSP), Caen: France, July 3-5, 2013.
- [Tutorial] 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
25-30, 2012, (the slides can be found here).
Peer-reviewed conferences
- C. Ye, K. Slavakis, J. Nakuci, S. F. Muldoon, and J. Medaglia. Online classification of dynamic multilayer-network time series in Riemannian manifolds. In Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3815-3819, Toronto: Canada, June 6-11, 2021.
- K. Slavakis and M. Yukawa. Outlier-robust kernel hierarchical-optimization RLS on a budget with affine constraints. In Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5335-5339, Toronto: Canada, June 6-11, 2021.
- G. Shetty, K. Slavakis, U. Nakarmi, G. Scutari, and L. Ying. Kernel bi-linear modeling for reconstructing data on manifolds: The dynamic-MRI case. In Proc. of the 2020 European Signal Processing Conference (EUSIPCO), pp. 1482-1486, Amsterdam: The Netherlands, January 2021.
- C. Ye, K. Slavakis, S. Muldoon, J. Nakuci and
J. Medaglia. Fast sequential multilayer brain-network state clustering. Presented at the Northeast Regional Conference on Complex Systems (NERCCS), Buffalo: NY: USA, April 1-3, 2020.
- K. Slavakis. Stochastic composite convex minimization with
affine constraints. In Proc. of the Asilomar Conference
on Signals, Systems and Computers, Pacific Grove, California, pp. 1871-1875,
Oct. 28-31, 2018.
- U. Nakarmi, K. Slavakis, H. Li, C. Zhang, P. Huang, S. Gaire, and
L. Ying. MLS: Self-learned joint manifold geometry and sparsity aware
framework for highly accelerated cardiac cine imaging. In Proc. of
the joint annual meeting ISMRM-ESMRMB, Paris: France, June 16-21, 2018.
- K. Slavakis, A. Konar, and N. Sidiropoulos. Fast
projection-based solvers for the non-convex quadratically constrained
feasibility problem. In Proc. of the IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), Calgary: Alberta: Canada, April 15-20,
2018.
- U. Nakarmi, K. Slavakis, and L. Ying. MLS: Joint
manifold-learning and sparsity-aware framework for highly accelerated
dynamic magnetic resonance imaging. In Proc. of the IEEE
International Symposium on Biomedical Imaging (ISBI), Washington DC:
USA, April 4-7, 2018.
- K. Slavakis, G. N. Shetty, A. Bose, U. Nakarmi and
L. Ying. Bi-linear modeling of manifold-data geometry for
dynamic-MRI recovery. In Proc. of the IEEE International Workshop on Computational
Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao:
Dutch Antilles, Dec. 10-13, 2017.
- K. Slavakis, S. Salsabilian, D. S. Wack, S. F. Muldoon,
H. Baidoo-Williams, J. Vettel, M. Cieslak and
S. Grafton. Riemannian multi-manifold modeling and clustering in
brain networks. In Proc. of SPIE Optics + Photonics, San Diego:
California: USA, 6-10 Aug., 2017.
- U. Nakarmi, K. Slavakis, J. Lyu, C. Zhang and L. Ying. Beyond
low-rank 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, 22-27 April, 2017.
- U. Nakarmi, K. Slavakis, J. Lyu and L. Ying. M-MRI: A
manifold-based framework to highly accelerated dynamic magnetic
resonance imaging. In Proc. of the International Symposium on
Biomedical Imaging (ISBI), Melbourne: Australia, 18-21 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. 5-9, 2017.
- K. Slavakis, S. Salsabilian, D. S. Wack, and S. F. Muldoon,
H. Baidoo-Williams, J. Vettel, M. Cieslak, S. Grafton. Clustering
brain-network-connectivity states using kernel partial correlations. In
Proc. of the 50th Asilomar Conference on Signals, Systems and Computers,
Pacific Grove, California, Nov. 6-9, 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. 6-11, 2016.
- K. Slavakis, S. Salsabilian, D. S. Wack, and S. F. Muldoon. Clustering
time-varying connectivity networks by Riemannian geometry: The brain-network
case. In Proc. of Statistical Signal Processing (SSP), Palma de
Mallorca: Spain, June 26-29, 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 13-16, 2016.
- G. V. Karanikolas, G. B. Giannakis, K. Slavakis, and
R. M. Leahy. Multi-kernel based nonlinear models for connectivity
identification of brain networks. In Proc. of ICASSP, Shanghai: China,
Mar. 25-30, 2016.
- P. A. Traganitis, K. Slavakis, and G. B. Giannakis. Large-scale subspace
clustering using random sketching and validation. In Proc. of the
Asilomar Conference on Signals, Systems, and Computers, Nov. 8-11,
2015.
- X. Wang, K. Slavakis, and G. Lerman. Multi-manifold modeling in
non-Euclidean spaces. In Proc. of the International Conference on
Artificial Intelligence and Statistics (AISTATS), PMLR 38:1023-1032, 2015.
- P. A. Traganitis, K. Slavakis, and G. B. Giannakis. Spectral clustering
of large-scale communities via random sketching and validation. Presented at
the Conference on Information Systems and Sciences (CISS), Baltimore, Maryland,
Mar. 18-20, 2015.
- P. A. Traganitis, K. Slavakis, and G. B. Giannakis. Clustering
high-dimensional data via random sampling and consensus. Presented at
GlobalSIP, Dec. 3-5, 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. 2-5, 2014.
- K. Slavakis, X. Wang, and G. Lerman. Clustering high-dimensional
dynamical systems on low-rank matrix manifolds. Presented at the Asilomar
Conference on Signals, Systems, and Computers, Nov. 2-5, 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. 26-30, 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 4-9, 2014 (Special session: "Signal processing for big
data").
- M. Zamanighomi, Z. Wang, K. Slavakis, and G. B. Giannakis. Linear minimum
mean-square error estimation based on high-dimensional data with missing
values. In Proc. of 48th Annual Conference on Information Sciences and
Systems (CISS), Princeton University: USA, Mar. 19-21, 2014.
- K. Slavakis, Y. Kopsinis, S. Theodoridis. New operators for fixed-point
theory: The sparsity-aware 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. 9-13, 2013.
- K. Slavakis, Y. Kopsinis, S. Theodoridis, G. B. Giannakis, and
V. Kekatos. Generalized iterative thresholding for sparsity-aware online
Volterra system identification. In Proc. of International Symposium on
Wireless Communication Systems (ISWCS), Ilmenau: Germany, Aug. 27-30, 2013.
- S. Theodoridis, Y. Kopsinis, K. Slavakis, and
S. Chouvardas. Sparsity-aware 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 3-5,
2013, (plenary paper).
- K. Slavakis, G. Leus, and G. B. Giannakis. Online robust portfolio risk
management using total least-squares and parallel splitting algorithms.
In Proc. of ICASSP, Vancouver: Canada, May 26-31, 2013.
- Y. Kopsinis, K. Slavakis, S. Theodoridis, and
S. McLaughlin. Thresholding-based sparsity-promoting online algorithms of
low complexity. In Proc. of ISCAS, Beijing, China, May 19-23, 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. 20-22, 2013.
- S. Chouvardas, K. Slavakis, Y. Kopsinis, and
S. Theodoridis. Sparsity-promoting adaptive algorithm for distributed
learning in diffusion networks. In Proceedings of the European Signal
Processing Conference (EUSIPCO), Bucharest: Romania, Aug. 27-31, 2012.
- Y. Kopsinis, K. Slavakis, S. Theodoridis, and S. McLaughlin. Generalized
thresholding sparsity-aware algorithm for low complexity online learning.
In Proceedings of the IEEE International Conference on Acoustics, Speech, and
Signal Processing (ICASSP), pp. 3277-3280, Kyoto: Japan, March 25-30,
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 "Sparsity-aware signal processing", Corfu:
Greece, July 6-8, 2011, (Invited).
- K. Slavakis, Y. Kopsinis, and S. Theodoridis. Revisiting adaptive
least-squares estimation and application to online sparse signal recovery.
In Proceedings of the IEEE International Conference on Acoustics, Speech, and
Signal Processing (ICASSP), pp. 4292-4295, Prague: Czech Republic, May 22-27,
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. 2048-2051, Prague: Czech Republic, May 22-27,
2011.
- K. Slavakis, S. Theodoridis, and I. Yamada. Low complexity
projection-based 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 7-10, 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. 2660-2663, Istanbul:
Turkey, August 23-26, 2010 (best-paper 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. 393-398, Italy, June 14-16, 2010 (best-student-paper
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. 3742-3745, Dallas: Texas: USA, March 14-19,
2010.
- M. Yukawa, K. Slavakis, and I. Yamada. Multi-domain adaptive filtering by
feasibility splitting. In Proceedings of the IEEE International Conference
on Acoustics, Speech, and Signal Processing (ICASSP), Dallas: Texas: USA, March
14-19, 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 5-7, 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. 221-224,
Perugia: Italy, June 21-24, 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. 1573-1576,
Taipei, April 19-24, 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. 32-37, Cancun: Mexico, October 16-19, 2008.
- K. Slavakis, S. Theodoridis, and I. Yamada. Robust adaptive nonlinear
beamforming by kernels and projection mappings. In Proceedings of EUSIPCO,
Lausanne: Switzerland, August 25-29, 2008.
- K. Slavakis and S. Theodoridis. Optimal sliding window sparsification
for online kernel-based classification by projections. In Proceedings of the
IAPR Cognitive Information Processing (CIP) Workshop, Santorini: Greece,
pp. 30-35, June 2008.
- K. Slavakis and S. Theodoridis. Sliding window online kernel-based
classification by projection mappings. In Proceedings of the IEEE ISCAS,
Seattle: USA, pp. 49-52, May 2008.
- F. Fourli-Kartsouni, 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. 299-308, 2007.
- K. Slavakis, S. Theodoridis, and I. Yamada. Online sparse kernel-based
classification by projections. In Proceedings of the IEEE Machine Learning
for Signal Processing (MLSP), Thessaloniki: Greece, pp. 294-299, August 2007.
- K. Slavakis, S. Theodoridis, and I. Yamada. Online kernel-based
classification by projections. In Proceedings of the IEEE ICASSP, Hawaii:
USA, vol. II, pp. 425-428, 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. 1005-1008, May 2006.
- M. Yukawa, K. Slavakis, and I. Yamada. Stereo echo canceler by adaptive
projected subgradient method with multiple room-acoustics information. In
Proceedings of the IWAENC, S03-15, pp. 185-188, 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
wavelets-Biorthogonal 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. 981-984, 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. 10-15, 2013.
Preprints / Technical reports
- X. Wang, K. Slavakis, and G. Lerman. Riemannian multi-manifold
modeling. 2014. [arXiv]
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