Enhancing the correntropy MACE filter with random projections

Neurocomputing

Volumn 72, Issue 1-3, 2008, Pages 102-111

  Jeong, Kyu Hwa ^a   Principe, Jose C ^b  

^a INTEL CORPORATION   (United States)

^b University of Florida   (United States)

Author keywords

Correntropy; Dimensionality reduction; Face recognition; Minimum average correlation energy; Pattern recognition; Random projection

Indexed keywords

COMPUTATIONAL COMPLEXITY; CORRELATION METHODS; DATA STORAGE EQUIPMENT; ELECTRON ENERGY LEVELS; FEATURE EXTRACTION; LEARNING SYSTEMS; OBJECT RECOGNITION; PATTERN RECOGNITION; PINCH EFFECT; PROBABILITY DENSITY FUNCTION; RANDOM PROCESSES; WAVE FILTERS;

APPLICATIONS.; COMPUTATIONAL COSTS; CORRELATION FILTERS; CORRENTROPY; CORRENTROPY MACES; CRITICAL ISSUES; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION METHODS; DIMENSIONALITY REDUCTIONS; FEATURE SPACES; HIGH DIMENSIONALITIES; HIGHER ORDER MOMENT INFORMATIONS; IMAGE DATUMS; MACHINE LEARNINGS; MINIMUM AVERAGE CORRELATION ENERGIES; MINIMUM AVERAGE CORRELATION ENERGY; NONLINEAR EXTENSIONS; POSITIVE DEFINITE FUNCTIONS; RANDOM PROJECTION; RANDOM PROJECTIONS; SIGNAL STRUCTURES; SPATIAL MATCHED FILTERS;

FACE RECOGNITION;

ARTICLE; CONTROLLED STUDY; CORRELATION ANALYSIS; ENTROPY; FACIAL EXPRESSION; IMAGE ANALYSIS; IMAGE PROCESSING; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MONTE CARLO METHOD; NONLINEAR SYSTEM; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; SIGNAL NOISE RATIO; SIGNAL PROCESSING;

EID: 55949131580     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.12.045     Document Type: Article

References (45)

1. D. Achlioptas, Database-friendly random projections, in: Symposium on Principles of Database Systems (PODS), 2001, pp. 274-281.
2. Achlioptas D. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66 (2003) 671-687
3. E. Brigham, H. Maninila, Random projection in dimensionality reduction: applications to image and text data, in: Proceedings of the Conference on Knowledge Discovery and Data Mining, vol. 16, 2001, pp. 245-250.
4. E. Candes, Compressive sampling, in: Proceedings of the Internetional Congress of Mathematics, Madrid, Spain, 2006.
5. Candes E., Romberg J., and Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52 2 (2006) 489-509
6. Candes E.J., and Tao T. Near-optimal signal recovery from random projections: universal encoding strategies?. IEEE Trans. Inf. Theory 52 12 (2006) 5406-5425
7. S. Dasgupta, Experiments with random projection, in: Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2000.
8. S. Dasgupta, A. Gupta, An elementary proof of the Johnson-Lindenstrauss lemma, Technical Report TR-99-006, International Computer Science Institute, Berkeley, CA, 1999.
9. S. Deegalla, H. Bostrom, Reducing high-dimensional data by principal component analysis vs. random projection for nearest neighbor classification, in: Proceedings of the International Conference on Machine Learning and Applications (ICMLA), 2006, pp. 245-250.
10. Donoho D. Compressed sensing. IEEE Trans. Inf. Theory 52 4 (2006) 1289-1306
11. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), Wiley, New York
12. Erdogmus D., Rao Y., Peddaneni H., Hegde A., and C Principe J. Recursive principal components analysis usingeigenvector matrix perterbation. EURASIP J. Appl. Signal Process. 13 (2004) 2034-2041
13. Fisher J.W., and Principe J.C. Recent advances to nonlinear MACE filters. Opt. Eng. 36 10 (1998) 2697-2709
14. D. Fradkin, D. Madigan, Experiments with random projection for machine learning, in: Proceedings of the Conference on Knowledge Discovery and Data Mining, 2003, pp. 517-522.
15. K. Fukunaga, Introduction to Statistical Pattern Recognition, second ed. Academic Press Professional, 1990.
16. N. Goel, G. Bebis, A. Nefian, Face recognition experiments with random projection, in: Proceedings of the SPIE, Biometric Technology for Human Identification, vol. II, March 2005, pp. 426-437.
17. Golub G.H., van Loan C.F., and Matrix Computations. Matrix Computations (1983), North Oxford Academic, Oxford, UK
18. Gonzalez R.C., and Woods R.E. Digital Image Processing. second ed. (2002), Prentice-Hall, Englewood Cliffs, NJ
19. Hecht-Nielsen R. Context Vectors: General Purpose Approximate Meaning Representations Self-organized from Raw Data (1994), IEEE Press
20. Hester C.F., and Casasent D. Multivariant technique for multiclass pattern rcognition. Appl. Opt. 19 (1980) 1758-1761
21. K.H. Jeong, S. Han, J.C. Principe, The fast correntropy MACE filter, in: Proceedings of the International Conference on Acoustics, Speech, Signal Processing (ICASSP), April 2007, pp. II - 613-616.
22. K.H. Jeong, W. Liu, S. Han, E. Hasanbelliu, J.C. Princiep, The correntropy MACE filter, in: Pattern Recognition in press.
23. K.H. Jeong, P.P. Pokharel, J. Xu, S. Han, J.C. Principe, Kernel synthetic distriminant function for object recognition, in: Proceedings of the International Conference on Acoustics, Speech, Signal Processing (ICASSP), vol. 5, France, May 2006, pp. 765-768.
24. K.H. Jeong, J.C. Principe, The correntropy MACE filter for image recognition, in: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Ireland, July 2006, pp. 9-14.
25. S. Kaski, Dimensionality reduction by random mapping: fast silimarity computation for clustering, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1998, pp. 413-418.
26. H. Kwon, N.M. Nasrabadi, Kernel matched signal detectors for hyperspectral tareget detection, in: Proceedings of the International Conference on Acoustics, Speech, Singal Processing (ICASSP), vol. 4, 2005, pp. 665-668.
27. W. Liu, P.P. Pokharel, J.C. Principe, Correntropy: a localized similarity measure, in: Proceedings of the 2006 IEEE World Congress on Computational Intelligence (WCCI), Canada, July 2006, pp. 10018-10023.
28. Liu W., Pokharel P.P., and Principe J.C. Correntropy: properties and applications in non-Gaussian signal processing. IEEE Trans. Signal Process. 55 11 (2007) 5286
29. Mahalanobis A., Vijaya Kumar B.V.K., and Casasent D. Minimum average correlation energy filters. Appl. Opt. 26 17 (1987) 3640
30. P.P. Pokharel, R. Agrawal, J.C. Principe, Correntropy based matched filtering, in: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), September 2005, pp. 148-155.
31. P.P. Pokharel, J.W. Xu, D. Erdogmus, J.C. Principe, A closed form solution for a nonlinear wiener filter, in: Proceedings of the International Conference on Acoustics, Speech, Signal Processing (ICASSP), vol. 3, France, May 2006, pp. 720-723.
32. Refregier Ph., and Figue J. Optimal trade-off filter for pattern recognition and their comparison with weiner approach. Opt. Comput. Process. 1 (1991) 3-10
33. Santamaría I., Pokharel P.P., and Principe J.C. Generalized correlation function: definition. IEEE Trans. Signal Process. 54 6 (2006) 2187-2197
34. M. Savvides, B.V.K. Vijaya Kumar, P.K. Khosla, Face verification using correlation filters, in: Proceedings of the Third IEEE Automatic Identification Advanced Technologies, Tarrytown, NY, 2002, pp. 56-61.
35. Scholkopf B., and Smola A.J. Learning with Kernels (2002), MIT Press, Cambridge, MA
36. Silverman B.W. Density Estimation for Statistics and Data Analysis (1986), CRC Press, Boca Ration, FL
37. Turin G.L. An introduction to matched filters. IEEE Trans. Inf. Theory 6 (1960) 311-329
38. Turk M., and Pentland A. Eigenfaces for recognition. J. Cognitive Neurosci. 3 1 (1991) 71-86
39. A. VanderLugt, Signal detection by complex spatial filtering, IEEE Trans. Inf. Theory (10) (1964) 139-145.
40. Vijaya Kumar B.V.K. Minimum variance synthetic discriminant functions. J. Opt. Soc. Am. A 3 10 (1986) 1579-1584
41. Vijaya Kumar B.V.K. Tutorial survey of composite filter designs for optical correlators. Appl. Opt. 31 (1992) 4773-4801
42. Vijaya Kumar B.V.K., Mahalanobis A., and Juday R.D. Correlation Pattern Recognition (2005), Cambridge University Press, Cambridge
43. Vijaya Kumar B.V.K., Savvides M., and Xie C. Correlation pattern recognition for face recognition. Proc. IEEE 94 11 (2006) 1963-1976
44. Vijaya Kumar B.V.K., Savvides M., Xie C., and Venkataramani K. Biometric verification with correlation filters. Appl. Opt. 43 2 (2004) 391-402
45. 〈http://www.amp.ece.cmu.edu〉: advanced multimedia processing lab at electrical and computer eng., CMU.