Sparse representation of data

Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010

Volumn , Issue , 2010, Pages 225-234

  Villmann, Thomas ^a   Schleif, Frank Michael ^b   Hammer, Barbara ^b  

^a UNIVERSITY OF APPLIED SCIENCES MITTWEIDA   (Germany)

^b BIELEFELD UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DIMENSIONALITY REDUCTION; FACTOR ANALYSIS METHOD; FUNDAMENTAL PRINCIPLES; INFORMATION PROCESSING MODELS; LATENT SEMANTIC INDEXING; MULTIMEDIA TECHNOLOGIES; RELEVANCE VECTOR MACHINE; SPARSE REPRESENTATION;

BAYESIAN NETWORKS; BIOINFORMATICS; DATA PROCESSING; LEARNING SYSTEMS; MULTIMEDIA SYSTEMS; NEURAL NETWORKS;

FACE RECOGNITION;

EID: 84887011236     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (59)

1. N. Alex, A. Hasenfuss, and B. Hammer. Patch clustering for massive data sets. Neurocomputing, 72(7-9):1455-1469, 2009.
2. C. Alzate and J. Suykens. Highly sparse kernel spectral clustering with predictive out-of-sample extensions. In this volume.
3. C. Alzate and J. Suykens. Kernel component analysis using an epsilon-insensitive robust loss function. IEEE Transactions on Neural Networks, 19(9):1583-1598, 2008.
4. C. Archambeau, M. Biga Diambeidou, S. Dablemont, G. de Lannoy, N. Delannay, N. Donckers, D. Francois, C. Krier, J. Lee, G. Simon, and F. Vrins. Blind prediction of ESANN time series, 2006. Talk at ESANN'06 accompanying a reviewed and conditionally accepted contribution (with very hard conditions which could hardly be fulfilled).
5. B. Arnonkijpanich, A. Hasenfuss, and B. Hammer. Local matrix adaptation in clustering and applications for manifold visualization. Neural Networks, to appear.
6. D. Balakrishnan, S. andf Madigan. Algorithms for sparse linear classifiers in the massive data setting. Journal of Machine Learning Research, 9:313-337, 2008.
7. A. Chichocki, R. Zdunek, A. Phan, and S.-I. Amari. Nonnegative matrix and tensor factorization. Wiley, 2009.
8. A. D'Aspremont, F. Bach, and L. El Ghaoui. Optimal solutions for sparse principal component analysis. Journal of Machine Learning Research, 9:1269-1294, 2008.
9. C. Dhanjal, S. Gunn, and J. Shawe-Taylor. Efficient sparse kernel feature extraction based on partial least squares. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(8):1347-1361, 2009.
10. D. Donoho. Compressed sensing. IEEE Transactions on Information Theory, 52:1289-1306, 2006.
11. D. Dornbusch, R. Haschke, S. Menzel, and H. Wersing. Finding correlations in multimodal data using decomposition approaches. In this volume.
12. Duarte-Carvajalino and G. J.M. Sapiro. Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization. IEEE Transactions on Image Processing, 18(7):1395-1408, 2009.
13. J. Duchi and Y. Singer. Efficient online and batch learning using forward backward splitting. Journal of Machine Learning Research, 10:2899-2934, 2009.
14. M. Elad and I. Yavneh. A plurality of sparse representations is better than the sparsest one alone. IEEE Transactions on Information Theory, 55(10):4701-4714, 2009.
15. J. B. Estrach and S. Mallat. Geometric models with co-occurrence groups. In this volume.
16. B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315:972-976, 2007.
17. J. Gao, J. Zhang, and D. Tien. Relevance units latent variable model and nonlinear dimensionality reduction. IEEE Transactions on Neural Networks, 21(1):123-135, 2010.
18. P. J. Gao and Kwan and D. Shi. Sparse kernel learning with lasso and bayesian inference algorithm. Neural Networks, 23(2):257-264, 2010.
19. Gisbrecht, B. Mokbel, and B. Hammer. Relational generative topographic mapping. In this volume.
20. B. Hammer and A. Hasenfuss. Clustering very large dissimilarity data sets. In N. El Gayar and F. Schwenker, editors, ANNPR'2010, volume 5998 of Lecture Notes in Artificial Intelligence, pages 259-273. Springer, 2010.
21. B. Hammer, A. Hasenfuss, and F. Rossi. Median topographic maps for biological data sets. In M. Biehl, B. Hammer, M. Verleysen, and T. Villmann, editors, Similarity Based Clustering, Lecture Notes Artificial Intelligence Vol. 5400, pages 92-117. Springer, 2009.
22. B. Hammer and T. Villmann. Generalized relevance learning vector quantization. Neural Networks, 15(8-9):1059-1068, 2002.
23. M. Hu, Y. Chen, and J.-Y. Kwok. Building sparse multiple-kernel svm classifiers. IEEE Transactions on Neural Networks, 20(5):827-839, 2009.
24. N. Hurley and S. Rickard. Comparing measures of sparsity. IEEE Transactions on Information Theory, 55(10):4723-4741, 2009.
25. M. Jafari, E. Vincent, S. Abdallah, M. Plumbley, and M. Davies. An adaptive stereo basis method for convolutive blind audio source separation. Neurocomputing, 71(10-12):2087-2097, 2008.
26. T. Kierzmann, S. Lange, and M. Riedmiller. Incremental grlvq: Learning relevant features for 3d object recognition. Neurocomputing, 71(13-15):2868-2879, 2008.
27. K. Labusch, E. Barth, and T. Martinetz. Simple method for high-performance digit recognition based on sparse coding. IEEE Transactions on Neural Networks, 19(11):1985-1989, 2008.
28. K. Labusch, E. Barth, and T. Martinetz. Sparse coding neural gas: Learning of overcomplete data representations. Neurocomputing, 72(7-9):1547-1555, 2009.
29. K. Labusch and T. Martinetz. Learning sparse codes for image reconstruction. In this volume.
30. S. Lange and M. Riedmiller. Deep learning visual control policies. In this volume.
31. J. Langford, L. Li, and T. Zhang. Sparse online learning via truncated gradient. Journal of Machine Learning Research, 10:777-801, 2009.
32. J. Lee and M. Verleysen. Nonlinear dimensionality reduction. Springer, 2007.
33. Y. Li, A. Cichocki, S.-I. Amari, S. Xie, and C. Guan. Equivalence probability and sparsity of two sparse solutions in sparse representation. IEEE Transactions on Neural Networks, 19(12):2009-2021, 2008.
34. J. Lücke and M. Sahani. Maximal causes for non-linear component extraction. Journal of Machine Learning Research, 9:1227-1267, 2008.
35. L. Ma and L. Zhang. Overcomplete topographic independent component analysis. Neurocomputing, 71(10-12):2217-2223, 2008.
36. J. Miao, L. Duan, L. Qing, W. Gao, X. Chen, and Y. Yuan. Spatial relationship representation for visual object searching. Neurocomputing, 71(10-12):1813-1823, 2008.
37. F. Movahedi Naini, G. Hosein Mohimani, M. Babaie-Zadeh, and C. Jutten. Estimating the mixing matrix in sparse component analysis (sca) based on partial k-dimensional subspace clustering. Neurocomputing, 71(10-12):2330-2343, 2008.
38. E. Mwebaze, P. Schneider, F.-M. Schleif, S. Haase, and T. Villmann. Divergence based learning vector quantization. In this volume.
39. P. O'Grady and B. Pearlmutter. Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint. Neurocomputing, 72(1-3):88-101, 2008.
40. B. Olshausen and D. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381:607-609, 1996.
41. R. Pashaie and N. Farhat. Self-organization in a parametrically coupled logistic map network: A model for information processing in the visual cortex. IEEE Transactions on Neural Networks, 20(4):597-608, 2009.
42. Q. Shi, J. Petterson, G. Dror, J. Langford, A. Smola, and S. Vishwanathan. Hash kernels for structured data. Journal of Machine Learning Research, 10:2615-2637, 2009.
43. K. Stadlthanner, F. Theis, E. Lang, A. Tome, C. Puntonet, and J. Gorriz. Hybridizing sparse component analysis with genetic algorithms for microarray analysis. Neurocomputing, 71(10-12):2356-2376, 2008.
44. J. Tikka and J. Hollmen. Sequential input selection algorithm for long-term prediction of time series. Neurocomputing, 71(13-15):2604-2615, 2008.
45. P. Tseng. Further results on stable recovery of sparse overcomplete representations in the presence of noise. IEEE Transactions on Information Theory, 55(2):888-899, 2009.
46. D. Tzikas, A. Likas, and N. Galatsanos. Sparse bayesian modeling with adaptive kernel learning. IEEE Transactions on Neural Networks, 20(6):926-937, 2009.
47. D. Tzikas, A. Likas, and N. Galatsanos. Variational bayesian sparse kernel-based blind image deconvolution with student's-t priors. IEEE Transactions on Image Processing, 18(4):753-764, 2009.
48. L. van der Maaten and G. Hinton. Visualizing high-dimensional data using t-sne. Journal of Machine Learning Research, 9:2579-2605, 2008.
49. M. van Gerven, A. Bahramisharif, T. Heskes, and O. Jensen. Selecting features for bci control based on a covert spatial attention paradigm. Neural Networks, 22(9):1271-1277, 2009.
50. Z.-M. Wang and W.-W. Wang. Fast and adaptive method for sar superresolution imaging based on point scattering model and optimal basis selection. IEEE Transactions on Image Processing, 18(7):1477-1486, 2009.
51. Y. Washizawa and A. Cichocki. Sparse blind identification and separation by using adaptive k-orthodrome clustering. Neurocomputing, 71(10-12):2321-2329, 2008.
52. G. Wen. Relative transformation-based neighborhood optimization for isometric embedding. Neurocomputing, 72(4-6):1205-1213, 2009.
53. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2):210-227, 2009.
54. J.-B. Yang, K.-Q. Shen, C.-J. Ong, and X.-P. Li. Feature selection for mlp neural network: The use of random permutation of probabilistic outputs. IEEE Transactions on Neural Networks, 20(12):1911-1922, 2009.
55. X. Zeng, S. Luo, and Q. Li. An associative sparse coding neural network and applications. Neurocomputing, 73(4-6):684-689, 2010.
56. L. Zhang and W. Zhou. On the sparseness of 1-norm support vector machines. Neural Networks.
57. T. Zhang. On the consistency of feature selection using greedy least squares regression. Journal of Machine Learning Research, 10:555-568, 2009.
58. S. Zhou, J. Lafferty, and L. Wasserman. Compressed and privacy-sensitive sparse regression. IEEE Transactions on Information Theory, 55(2):846-866, 2009.
59. D. Zühlke, F.-M. Schleif, T. Geweniger, S. Haase, and T. Villmann. Learning vector quantization for heterogeneous structured data. In this volume.