{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:08:28Z","timestamp":1742947708988,"version":"3.40.3"},"publisher-location":"Berlin, Heidelberg","reference-count":23,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783662448472"},{"type":"electronic","value":"9783662448489"}],"license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-662-44848-9_15","type":"book-chapter","created":{"date-parts":[[2014,9,1]],"date-time":"2014-09-01T01:42:21Z","timestamp":1409535741000},"page":"227-241","source":"Crossref","is-referenced-by-count":7,"title":["Anomaly Detection with Score Functions Based on the Reconstruction Error of the Kernel PCA"],"prefix":"10.1007","author":[{"given":"Laetitia","family":"Chapel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chlo\u00e9","family":"Friguet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv.\u00a041(3), 15:1\u201315:58 (2009)","DOI":"10.1145\/1541880.1541882"},{"issue":"12","key":"15_CR2","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1016\/j.sigpro.2003.07.018","volume":"83","author":"M. Markou","year":"2003","unstructured":"Markou, M., Singh, S.: Novelty detection: a review - part 1: statistical approaches. Signal Processing\u00a083(12), 2481\u20132497 (2003)","journal-title":"Signal Processing"},{"issue":"5","key":"15_CR3","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/sam.11161","volume":"5","author":"A. Zimek","year":"2012","unstructured":"Zimek, A., Schubert, E., Kriegel, H.P.: A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining\u00a05(5), 363\u2013387 (2012)","journal-title":"Statistical Analysis and Data Mining"},{"key":"15_CR4","unstructured":"Williams, C., Seeger, M.: The effect of the input density distribution on kernel-based classifiers. In: Proceedings of the 17th International Conference on Machine Learning, pp. 1159\u20131166 (2000)"},{"issue":"7","key":"15_CR5","doi-asserted-by":"publisher","first-page":"2510","DOI":"10.1109\/TIT.2005.850052","volume":"51","author":"J. Shawe-Taylor","year":"2005","unstructured":"Shawe-Taylor, J., Williams, C.K., Cristianini, N., Kandola, J.: On the eigenspectrum of the Gram matrix and the generalization error of kernel-PCA. IEEE Transactions on Information Theory\u00a051(7), 2510\u20132522 (2005)","journal-title":"IEEE Transactions on Information Theory"},{"key":"15_CR6","volume-title":"The numerical treatment of integral equations","author":"C.T. Baker","year":"1977","unstructured":"Baker, C.T.: The numerical treatment of integral equations, vol.\u00a013. Clarendon Press, Oxford (1977)"},{"issue":"3","key":"15_CR7","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1162\/089976602317250942","volume":"14","author":"M. Girolami","year":"2002","unstructured":"Girolami, M.: Orthogonal series density estimation and the kernel eigenvalue problem. Neural Comput\u00a014(3), 669\u2013688 (2002)","journal-title":"Neural Comput"},{"issue":"2-3","key":"15_CR8","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/s10994-006-6895-9","volume":"66","author":"G. Blanchard","year":"2007","unstructured":"Blanchard, G., Bousquet, O., Zwald, L.: Statistical properties of kernel principal component analysis. Mach. Learn.\u00a066(2-3), 259\u2013294 (2007)","journal-title":"Mach. Learn."},{"key":"15_CR9","first-page":"1","volume":"3","author":"F.R. Bach","year":"2003","unstructured":"Bach, F.R., Jordan, M.I.: Kernel independent component analysis. The Journal of Machine Learning Research\u00a03, 1\u201348 (2003)","journal-title":"The Journal of Machine Learning Research"},{"issue":"4","key":"15_CR10","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/MSP.2013.2253631","volume":"30","author":"Z. Harchaoui","year":"2013","unstructured":"Harchaoui, Z., Bach, F., Capp\u00e9, O., Moulines, E.: Kernel-Based Methods for Hypothesis Testing: A Unified View. IEEE Signal Processing Magazine\u00a030(4), 87\u201397 (2013)","journal-title":"IEEE Signal Processing Magazine"},{"issue":"3","key":"15_CR11","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1016\/j.patcog.2006.07.009","volume":"40","author":"H. Hoffmann","year":"2007","unstructured":"Hoffmann, H.: Kernel PCA for novelty detection. Pattern Recognition\u00a040(3), 863\u2013874 (2007)","journal-title":"Pattern Recognition"},{"key":"15_CR12","unstructured":"Zhang, K., Zheng, V.W., Wang, Q., Kwok, J.T., Yang, Q., Marsic, I.: Covariate shift in hilbert space: A solution via surrogate kernels. In: Proceedings of the 30th International Conference on Machine Learning, vol.\u00a028, pp. 388\u2013395 (2013)"},{"issue":"6","key":"15_CR13","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1145\/1882261.1866190","volume":"29","author":"A.C. \u00d6ztireli","year":"2010","unstructured":"\u00d6ztireli, A.C., Alexa, M., Gross, M.: Spectral sampling of manifolds. ACM Transactions on Graphics (TOG)\u00a029(6), 168 (2010)","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"15_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/11784203_15","volume-title":"Advances in Computer Graphics","author":"R. Liu","year":"2006","unstructured":"Liu, R., Jain, V., Zhang, H.: Sub-sampling for efficient spectral mesh processing. In: Nishita, T., Peng, Q., Seidel, H.-P. (eds.) CGI 2006. LNCS, vol.\u00a04035, pp. 172\u2013184. Springer, Heidelberg (2006)"},{"key":"15_CR15","unstructured":"Zhao, M., Saligrama, V.: Anomaly detection with score functions based on nearest neighbor graphs. In: Advances in Neural Information Processing Systems 22, pp. 2250\u20132258 (2009)"},{"key":"15_CR16","unstructured":"Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Advances in Neural Information Processing Systems, pp. 478\u2013486 (2011)"},{"issue":"3-4","key":"15_CR17","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s007780050006","volume":"8","author":"E.M. Knorr","year":"2000","unstructured":"Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-based outliers: algorithms and applications. The VLDB Journal\u00a08(3-4), 237\u2013253 (2000)","journal-title":"The VLDB Journal"},{"issue":"7","key":"15_CR18","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B. Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation\u00a013(7), 1443\u20131471 (2001)","journal-title":"Neural Computation"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Schubert, E., Wojdanowski, R., Zimek, A., Kriegel, H.P.: On evaluation of outlier rankings and outlier scores. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 1047\u20131058 (2012)","DOI":"10.1137\/1.9781611972825.90"},{"key":"15_CR20","unstructured":"Caputo, B., Sim, K., Furesjo, F., Smola, A.: Appearance-based object recognition using SVMs: which kernel should I use? In: NIPS Workshop on Statistical Methods for Computational Experiments in Visual Processing and Computer Vision (2002)"},{"key":"15_CR21","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/72.914517","volume":"12","author":"K.R. M\u00fcller","year":"2001","unstructured":"M\u00fcller, K.R., Mika, S., R\u00e4tsch, G., Tsuda, K., Sch\u00f6lkopf, B.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks\u00a012, 181\u2013201 (2001)","journal-title":"IEEE Transactions on Neural Networks"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology\u00a02, 27:1\u201327:27 (2011), \n                    \n                      http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm","DOI":"10.1145\/1961189.1961199"},{"key":"15_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/3-540-49257-7_15","volume-title":"Database Theory - ICDT\u201999","author":"K. Beyer","year":"1998","unstructured":"Beyer, K., et al.: When is \u201cnearest neighbor\u201d meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol.\u00a01540, pp. 217\u2013235. Springer, Heidelberg (1998)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-662-44848-9_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,14]],"date-time":"2019-09-14T20:07:21Z","timestamp":1568491641000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-662-44848-9_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783662448472","9783662448489"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-662-44848-9_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2014]]}}}