Abstract
This work addresses the issue of separating two finite sets in by means of a suitable revolution cone The specific challenge at hand is to determine the aperture coefficient , the axis , and the apex of the cone. These parameters have to be selected in such a way as to meet certain optimal separation criteria. Part I of this work focusses on the homogeneous case in which the apex of the revolution cone is the origin of the space. The homogeneous case deserves a separated treatment, not just because of its intrinsic interest, but also because it helps to built up the general theory. Part II of this work concerns the non-homogeneous case in which the apex of the cone can move in some admissible region. The non-homogeneous case is structurally more involved and leads to challenging nonconvex nonsmooth optimization problems.
References
- L. T. H. An, P. D. Tao: A D.C. optimization algorithm for solving the trust-region subproblem, SIAM J. Optim. 8 (1998) 476–505.https://doi.org/10.1137/S1052623494274313Google Scholar
- A. Astorino, A. Fuduli, M. Gaudioso: DC models for spherical separation, J. Glob. Optim. 48 (2010) 657–669.https://doi.org/10.1007/s10898-010-9558-0Google Scholar
- A. Astorino, M. Gaudioso: Polyhedral separability through successive LP, J. Optim. Theory Appl. 112 (2002) 265–293.https://doi.org/10.1023/A:1013649822153Google Scholar
- A. Astorino, M. Gaudioso: Ellipsoidal separation for classification problems, Optim. Methods Softw. 20 (2005) 267–276.https://doi.org/10.1080/10556780512331318254Google Scholar
- A. Astorino, M. Gaudioso, A. Seeger: Conic separation of finite sets. II. The nonhomogeneous case, J. Convex Analysis 21(3) (2014), to appear.https://doi.org/10.68381/jca21044Google Scholar
- C. Bergeron, G. Moore, J. Zaretzki, C. M. Breneman, K. P. Bennett: Fast bundle algorithm for multiple instance learning, IEEE Trans. Pattern Anal. Mach. Intell. 34 (2011) 1068–1079,https://doi.org/10.1109/TPAMI.2011.194Google Scholar
- C.-C. Chang, C.-J. Lin: LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol. 2 (2011) 27:1–27:27; software available at: www.csie.ntu.edu.tw/~cjlin/libsvm.https://doi.org/10.1145/1961189.1961199Google Scholar
- O. Chapelle, A. Zien: Semi-supervised classification by low density separation, in: Proc. 10th Intern. Workshop on Artificial Intelligence and Statistics (2005) 57–64.Google Scholar
- P. H. Dien, G. Mastroeni, M. Pappalardo, P. H. Quang: Regularity conditions for constrained extremum problems via image space: the nonlinear case, J. Optim. Theory Appl. 80 (1994) 19–37.https://doi.org/10.1007/BF02196591Google Scholar
- J. E. Falk, V. E. Karlov: Robust separation of finite sets via quadratics, Comput. Oper. Res. 28(6) (2001) 537–561.https://doi.org/10.1016/S0305-0548(99)00134-3Google Scholar
- A. Fuduli, M. Gaudioso, G. Giallombardo: Minimizing nonconvex nonsmooth functions via cutting planes and proximity control, SIAM J. Optim. 14 (2004) 743–756.https://doi.org/10.1137/S1052623402411459Google Scholar
- R. N. Gasimov, G. Ozturk: Separation via polyhedral conic functions, Optim. Methods Softw. 21 (2006) 527–540.https://doi.org/10.1080/10556780600723252Google Scholar
- E. G. Gilbert, D. W. Johnson, S. S. Keerthi: A fast procedure for computing the distance between complex objects, IEEE J. Robot. Autom. 4 (1988) 193–203.https://doi.org/10.1109/56.2083Google Scholar
- Y.-J. Gotoh, H. Konno, S. Uryasev, A. Yuki: Failure discrimination by semidefinite programming, in: Financial Engineering, E-Commerce and Supply Chain, P. M. Pardalos, V. K. Tsitsiringos (eds.), Kluwer, Dordrecht (2002) 379–396.https://doi.org/10.1007/978-1-4757-5226-7_21Google Scholar
- M. I. Henig: A cone separation theorem, J. Optim. Theory Appl. 36 (1982) 451–455.https://doi.org/10.1007/BF00934357Google Scholar
- M. L. Hoai, A. L. T. Hoai, P. D. Tao, V. N. Ngai: An efficient DCA for spherical separation, in: Intelligent Information and Database Systems, Part II (Daegu, 2011), N. T. Nguyen et al. (eds.), Lecture Notes in Computer Science 6592, Springer, Berlin (2011) 421–431.https://doi.org/10.1007/978-3-642-20042-7_43Google Scholar
- R. Kasimbeyli: A nonlinear cone separation theorem and scalarization in nonconvex vector optimization, SIAM J. Optim. 20 (2009) 1591–1619.https://doi.org/10.1137/070694089Google Scholar
- K. Kim, H. S. Ryoo: Data separation via a finite number of discriminant functions: a global optimization approach, Appl. Math. Comput. 190 (2007) 476–489.https://doi.org/10.1016/j.amc.2007.01.051Google Scholar
- O. L. Mangasarian: Linear and nonlinear separation of patterns by linear programming, Oper. Res. 13 (1965) 444–452.https://doi.org/10.1287/opre.13.3.444Google Scholar
- G. Mastroeni, M. Pappalardo: Separation and regularity in the image space, in: New Trends in Mathematical Programming, F. Giannessi, S. Komlósi, T. Rapcsák (eds.), Kluwer, Dordrecht (1998) 181–190.https://doi.org/10.1007/978-1-4757-2878-1_14Google Scholar
- P. M. Murphy, D. W. Aha: UCI repository of machine learning databases, available at: www.ics.uci.edu/~mlearn/MLRepository.html (1992).Google Scholar
- S. Odewahn, E. Stockwell, R. Pennington, R. Humphreys, W. Zumach: Automated star/galaxy discrimination with neural networks, Astrophys. J. 103 (1992) 318–331.https://doi.org/10.1086/116063Google Scholar
- M. Pappalardo: On the image regularity condition, J. Optim. Theory Appl. 121 (2004) 673–678.https://doi.org/10.1023/B:JOTA.0000037687.36868.5aGoogle Scholar
- R. T. Rockafellar: Convex Analysis, Princeton Univ. Press, Princeton (1970).https://doi.org/10.1515/9781400873173Google Scholar
Suggested citation
Published by Heldermann Verlag, 2014. Rights now held by Banach Press LLC.
