Free Access
Issue
A&A
Volume 606, October 2017
Article Number A39
Number of page(s) 13
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/201730968
Published online 05 October 2017
  1. Agyemang, M., Barker, K., & Alhajj, R. 2006, Intell. Data Anal., 10, 521 [Google Scholar]
  2. Angiulli, F., Fassetti, F., & Palopoli, L. 2009, ACM Trans. Database Syst., 34, 7 [CrossRef] [Google Scholar]
  3. Banerji, M., McMahon, R. G., Hewett, P. C., Gonzalez-Solares, E., & Koposov, S. E. 2013, MNRAS, 429, L55 [NASA ADS] [CrossRef] [Google Scholar]
  4. Baron, D., & Poznanski, D. 2017, MNRAS, 465, 4530 [NASA ADS] [CrossRef] [Google Scholar]
  5. Basu, S., Bilenko, M., & Mooney, R. J. 2004, in Proc. Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04 (New York, NY, USA: ACM), 59 [Google Scholar]
  6. Batuwita, R., & Palade, V. 2013, Class Imbalance Learning Methods for Support Vector Machines (John Wiley and Sons, Inc.), 83 [Google Scholar]
  7. Beaumont, C. N., Williams, J. P., & Goodman, A. A. 2011, ApJ, 741, 14 [NASA ADS] [CrossRef] [Google Scholar]
  8. Beck, R., Dobos, L., Budavári, T., Szalay, A. S., & Csabai, I. 2016, MNRAS, 460, 1371 [NASA ADS] [CrossRef] [Google Scholar]
  9. Benjamin, R. A., Churchwell, E., Babler, B. L., et al. 2003, PASP, 115, 953 [NASA ADS] [CrossRef] [Google Scholar]
  10. Bilicki, M., Jarrett, T. H., Peacock, J. A., Cluver, M. E., & Steward, L. 2014, ApJS, 210, 9 [NASA ADS] [CrossRef] [Google Scholar]
  11. Bilicki, M., Peacock, J. A., Jarrett, T. H., et al. 2016, ApJS, 225, 5 [NASA ADS] [CrossRef] [Google Scholar]
  12. Blanton, M. R., & Roweis, S. 2007, AJ, 133, 734 [NASA ADS] [CrossRef] [Google Scholar]
  13. Bolton, A. S., Schlegel, D. J., Aubourg, É., et al. 2012, AJ, 144, 144 [NASA ADS] [CrossRef] [Google Scholar]
  14. Brandl, B. R., Bernard-Salas, J., Spoon, H. W. W., et al. 2006, ApJ, 653, 1129 [NASA ADS] [CrossRef] [Google Scholar]
  15. Cavuoti, S., Brescia, M., D’Abrusco, R., Longo, G., & Paolillo, M. 2014, MNRAS, 437, 968 [NASA ADS] [CrossRef] [Google Scholar]
  16. Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, ArXiv e-prints [arXiv:1612.05560] [Google Scholar]
  17. Chandola, V., Banerjee, A., & Kumar, V. 2009, ACM Comput. Surv., 41, 15 [CrossRef] [Google Scholar]
  18. Chapelle, O., & Zien, A. 2005, in AISTATS 2005, Max-Planck-Gesellschaft, 57 [Google Scholar]
  19. Chapelle, O., Schölkopf, B., & Zien, A. 2006, Semi-Supervised Learning, Adaptive computation and machine learning (Cambridge, USA: MIT Press), 508 [Google Scholar]
  20. Cluver, M. E., Jarrett, T. H., Hopkins, A. M., et al. 2014, ApJ, 782, 90 [NASA ADS] [CrossRef] [Google Scholar]
  21. Cortes, C., & Vapnik, V. 1995, Mach. Learn., 20, 273 [Google Scholar]
  22. Cutri, R. M., Wright, E. L., Conrow, T., et al. 2013, Explanatory Supplement to the AllWISE Data Release Products, Tech. rep., ed. R. M. Cutri et al. [Google Scholar]
  23. DESI Collaboration, Aghamousa, A., Aguilar, J., et al. 2016, ArXiv e-prints [arXiv:1611.00036] [Google Scholar]
  24. Donoso, E., Yan, L., Stern, D., & Assef, R. J. 2014, ApJ, 789, 44 [NASA ADS] [CrossRef] [Google Scholar]
  25. Fadely, R., Hogg, D. W., & Willman, B. 2012, ApJ, 760, 15 [NASA ADS] [CrossRef] [Google Scholar]
  26. Faherty, J. K., Alatalo, K., Anderson, L. D., et al. 2015, ArXiv e-prints [arXiv:1505.01923] [Google Scholar]
  27. Hambly, N. C., MacGillivray, H. T., Read, M. A., et al. 2001, MNRAS, 326, 1279 [NASA ADS] [CrossRef] [Google Scholar]
  28. Han, J., Kamber, M., & Pei, J. 2011, Data Mining: Concepts and Techniques, 3rd edn. (San Francisco, USA: Morgan Kaufmann Publishers Inc.) [Google Scholar]
  29. Hautamaki, V., Karkkainen, I., & Franti, P. 2004, in Proc. Pattern Recognition, 17th International Conference on (ICPR’04) Vol. 3, ICPR ’04 (Washington, DC, USA: IEEE Computer Society), 430 [Google Scholar]
  30. Hawkins, S., He, H., Williams, G. J., & Baxter, R. A. 2002, in Proc. 4th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2000 (London, UK: Springer-Verlag), 170 [Google Scholar]
  31. Heinis, S., Kumar, S., Gezari, S., et al. 2016, ApJ, 821, 86 [NASA ADS] [CrossRef] [Google Scholar]
  32. Ho, T. K. 1998, IEEE Trans. Pattern Anal. Mach. Intell., 20, 832 [CrossRef] [Google Scholar]
  33. Hodge, V., & Austin, J. 2004, Artif. Intell. Rev., 22, 85 [CrossRef] [Google Scholar]
  34. Hoffmann, H. 2007, Pattern Recogn., 40, 863 [CrossRef] [Google Scholar]
  35. Hoyle, B. 2016, Astron. Comput., 16, 34 [NASA ADS] [CrossRef] [Google Scholar]
  36. Jarrett, T. H., Chester, T., Cutri, R., et al. 2000, AJ, 119, 2498 [NASA ADS] [CrossRef] [Google Scholar]
  37. Jarrett, T. H., Cohen, M., Masci, F., et al. 2011, ApJ, 735, 112 [NASA ADS] [CrossRef] [Google Scholar]
  38. Jarrett, T. H., Cluver, M. E., Magoulas, C., et al. 2017, ApJ, 836, 182 [NASA ADS] [CrossRef] [Google Scholar]
  39. Jolliffe, I. 2002, Principal component analysis (New York: Springer Verlag) [Google Scholar]
  40. Kirkpatrick, J. D., Schneider, A., Fajardo-Acosta, S., et al. 2014, ApJ, 783, 122 [NASA ADS] [CrossRef] [Google Scholar]
  41. Kirkpatrick, J. D., Kellogg, K., Schneider, A. C., et al. 2016, ApJS, 224, 36 [NASA ADS] [CrossRef] [Google Scholar]
  42. Kovács, A., & Szapudi, I. 2015, MNRAS, 448, 1305 [NASA ADS] [CrossRef] [Google Scholar]
  43. Krakowski, T., Małek, K., Bilicki, M., et al. 2016, A&A, 596, A39 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Kriegel, H.-P., Kröger, P., & Zimek, A. 2009, ACM Trans. Knowl. Discov. Data, 3, 1 [CrossRef] [Google Scholar]
  45. Kurcz, A., Bilicki, M., Solarz, A., et al. 2016, A&A, 592, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Langone, R., Mall, R., Alzate, C., & Suykens, J. A. K. 2015, ArXiv e-prints [arXiv:1505.00477] [Google Scholar]
  47. Le, T., Tran, D., Ma, W., & Sharma, D. 2010, An optimal sphere and two large margins approach for novelty detection, 2010 Int. Joint Conf. Neural Network (IJCNN) [Google Scholar]
  48. Le, T., Tran, D., Ma, W., & Sharma, D. 2011, Multiple Distribution Data Description Learning Algorithm for Novelty Detection, Adv. Knowledge Discovery Data Mining, Proc., 246 [Google Scholar]
  49. Liu, Y.-H., Liu, Y.-C., & Chen, Y.-Z. 2011, Expert Syst. Appl., 38, 6222 [CrossRef] [Google Scholar]
  50. Mainzer, A., Bauer, J., Cutri, R. M., et al. 2014, ApJ, 792, 30 [NASA ADS] [CrossRef] [Google Scholar]
  51. Małek, K., Solarz, A., Pollo, A., et al. 2013, A&A, 557, A16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  52. Manevitz, L., & Yousef, M. 2007, Neurocomput., 70, 1466 [CrossRef] [Google Scholar]
  53. Markou, M., & Singh, S. 2003, Signal Processing, 83, 2499 [CrossRef] [Google Scholar]
  54. Marton, G., Tóth, L. V., Paladini, R., et al. 2016, MNRAS, 458, 3479 [NASA ADS] [CrossRef] [Google Scholar]
  55. Mateos, S., Alonso-Herrero, A., Carrera, F. J., et al. 2012, MNRAS, 426, 3271 [NASA ADS] [CrossRef] [Google Scholar]
  56. Meisner, A. M., Lang, D., & Schlegel, D. J. 2017a, ArXiv e-prints [arXiv:1705.06746] [Google Scholar]
  57. Meisner, A. M., Lang, D., & Schlegel, D. J. 2017b, AJ, 153, 38 [NASA ADS] [CrossRef] [Google Scholar]
  58. Mercer, J. 1909, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 209, 415 [CrossRef] [Google Scholar]
  59. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. 2015, e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, r package version 1.6-7 [Google Scholar]
  60. Mika, S., Rätsch, G., Weston, J., Schölkopf, B., & Müller, K.-R. 1999, in Proceedings of the 1999 IEEE Signal Processing Society Workshop, 9, Max-Planck-Gesellschaft (IEEE), 41 [Google Scholar]
  61. Murphy, K. P. 2012, Machine Learning: A Probabilistic Perspective (The MIT Press) [Google Scholar]
  62. Pollo, A., Rybka, P., & Takeuchi, T. T. 2010, A&A, 514, A3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  63. Prakash, A., Licquia, T. C., Newman, J. A., & Rao, S. M. 2015, ApJ, 803, 105 [NASA ADS] [CrossRef] [Google Scholar]
  64. R Core Team. 2013, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria [Google Scholar]
  65. Rahman, M., Ménard, B., & Scranton, R. 2016, MNRAS, 457, 3912 [NASA ADS] [CrossRef] [Google Scholar]
  66. Sangeetha, R., & Kalpana, B. 2010, A Comparative Study and Choice of an Appropriate Kernel for Support Vector Machines, eds. V. V. Das, & R. Vijaykumar (Berlin, Heidelberg: Springer Berlin Heidelberg), 549 [Google Scholar]
  67. Sauvage, M., Tuffs, R. J., & Popescu, C. C. 2005, Space Sci. Rev., 119, 313 [NASA ADS] [CrossRef] [Google Scholar]
  68. Schölkopf, B., Smola, A. J., & Müller, K.-R. 1999, in Advances in Kernel Methods, ed. B. Schölkopf, C. J. C. Burges, & A. J. Smola (Cambridge, USA: MIT Press), 327 [Google Scholar]
  69. Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. 2000, Adv. Neural Inf. Process. Syst., 582 [Google Scholar]
  70. SDSS Collaboration, Albareti, F. D., Allende Prieto, C., et al. 2016, ArXiv e-prints [arXiv:1608.02013] [Google Scholar]
  71. Secrest, N. J., Dudik, R. P., Dorland, B. N., et al. 2015, ApJS, 221, 12 [NASA ADS] [CrossRef] [Google Scholar]
  72. Shawe-Taylor, S., & Cristianini, N. 2004, Kernel Methods for Pattern Analysis (Cambridge, UK: Cambridge, UP) [Google Scholar]
  73. Shi, F., Liu, Y.-Y., Sun, G.-L., et al. 2015, MNRAS, 453, 122 [NASA ADS] [CrossRef] [Google Scholar]
  74. Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163 [NASA ADS] [CrossRef] [Google Scholar]
  75. Solarz, A., Pollo, A., Takeuchi, T. T., et al. 2012, A&A, 541, A50 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  76. Solarz, A., Pollo, A., Takeuchi, T. T., et al. 2015, A&A, 582, A58 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Stern, D., Assef, R. J., Benford, D. J., et al. 2012, ApJ, 753, 30 [NASA ADS] [CrossRef] [Google Scholar]
  78. Tax, D. M., & Duin, R. P. 2004, Mach. Learn., 54, 45 [CrossRef] [Google Scholar]
  79. Tax, D. M. J., & Duin, R. P. W. 1999, Patt. Recog. Lett., 20, 1191 [CrossRef] [Google Scholar]
  80. Taylor, M. B. 2005, in Astronomical Data Analysis Software and Systems XIV, eds. P. Shopbell, M. Britton, & R. Ebert, ASP Conf. Ser., 347, 29 [Google Scholar]
  81. Taylor, M. B. 2006, in Astronomical Data Analysis Software and Systems XV, eds. C. Gabriel, C. Arviset, D. Ponz, & S. Enrique, ASP Conf. Ser., 351, 666 [Google Scholar]
  82. Škoda, P., Shakurova, K., Koza, J., & Palička, A. 2016, ArXiv e-prints [arXiv:1612.07549] [Google Scholar]
  83. Škoda, P., Palička, A., Koza, J., & Shakurova, K. 2017, IAU Symp., 325, 180 [NASA ADS] [Google Scholar]
  84. Vapnik, V. N. 1995, The nature of statistical learning theory (New York, USA: Springer-Verlag New York, Inc.) [Google Scholar]
  85. Vapnik, V., & Chervonenkis, A. 1974, Theory of Pattern Recognition [in Russian] (Moscow: Nauka), (German Translation: W. Wapnik & A. Tscherwonenkis), Theorie der Zeichenerkennung, Akademie–Verlag, Berlin, 1979 [Google Scholar]
  86. Walker, H. J., Volk, K., Wainscoat, R. J., Schwartz, D. E., & Cohen, M. 1989, AJ, 98, 2163 [NASA ADS] [CrossRef] [Google Scholar]
  87. Wolf, C., Meisenheimer, K., Röser, H.-J., et al. 2001, A&A, 365, 681 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  88. Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868 [NASA ADS] [CrossRef] [Google Scholar]
  89. Yan, L., Donoso, E., Tsai, C.-W., et al. 2013, AJ, 145, 55 [NASA ADS] [CrossRef] [Google Scholar]
  90. Yang, J., & Wang, W. 2003, Cluseq: efficient and effective sequence clustering [Google Scholar]
  91. York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579 [NASA ADS] [CrossRef] [Google Scholar]
  92. Zhang, Y., & Zhao, Y. 2004, A&A, 422, 1113 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.