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G. Taubin (1995)
A signal processing approach to fair surface designProceedings of the 22nd annual conference on Computer graphics and interactive techniques
D. Zimmerman (1997)
Teacher’s Corner: A Note on Interpretation of the Paired-Samples t TestJournal of Educational Statistics, 22
Mingrui Wu, Jieping Ye (2009)
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with OutliersIEEE Transactions on Pattern Analysis and Machine Intelligence, 31
K. Hempstalk, E. Frank, I. Witten (2008)
One-Class Classification by Combining Density and Class Probability Estimation
Stéphane Lafon, Weiqiang Jing (2006)
Diffusion maps
M. Desbrun, Mark Meyer, P. Schröder, A. Barr (1999)
Implicit fairing of irregular meshes using diffusion and curvature flowProceedings of the 26th annual conference on Computer graphics and interactive techniques
D. Horn, A. Gottlieb (2001)
The Method of Quantum Clustering
Fei Liu, K. Ting, Zhi-Hua Zhou (2012)
Isolation-Based Anomaly DetectionACM Trans. Knowl. Discov. Data, 6
S. Papadimitriou, H. Kitagawa, Phillip Gibbons, C. Faloutsos (2003)
LOCI: fast outlier detection using the local correlation integralProceedings 19th International Conference on Data Engineering (Cat. No.03CH37405)
R. Badeau, B. David, G. Richard (2005)
Fast approximated power iteration subspace trackingIEEE Transactions on Signal Processing, 53
A. Grigor’yan (1999)
Spectral Theory and Geometry: Estimates of heat kernels on Riemannian manifolds
A. Singer, R. Coifman (2008)
Non-linear independent component analysis with diffusion mapsApplied and Computational Harmonic Analysis, 25
V. Pan, Zhao Chen (1999)
The complexity of the matrix eigenproblem
Huaijun Qiu, E. Hancock (2007)
Clustering and Embedding Using Commute TimesIEEE Transactions on Pattern Analysis and Machine Intelligence, 29
G. Blanchard, Gyemin Lee, C. Scott (2010)
Semi-Supervised Novelty DetectionJ. Mach. Learn. Res., 11
V. Chandola, A. Banerjee, Vipin Kumar (2009)
Anomaly detection: A surveyACM Comput. Surv., 41
(2005)
Gene Expression Model Selector
B. Aubert, S. Yellin, G. Simi, S. Trincaz-Duvoid, F. Diberder, K. Fratini, C. Brown, A. Boyarski, M. Momayezi, F. Brochard, M. Dima, M. Weaver, A. Gritsan, R. Schindler, V. Eschenburg, J. Branson, Y. Xie, S. McMahon, J. Oyang, C. Young, D. Best, C. Yéche, T. Schalk, C. Borean, R. Cahn, K. Arisaka, E. Lamanna, M. Iwasaki, H. Nicholson, A. Bukin, M. Falbo, R. Faccini, R. Potter, L. Buono, M. Krishnamurthy, K. Bibber, F. Ferroni, D. Lange, F. Porter, F. Muheim, D. Brown, T. Berger-Hryn'ova, T. Harrison, C. Buchanan, J. Brose, A. Lusiani, D. Piccolo, M. Haire, S. Dasu, J. Reidy, D. Judd, A. Palano, D. Lavin, N. Chevalier, G. Grosdidier, K. Goetzen, D. Knowles, C. Gatto, K. Peters, M. Piccolo, A. Borgland, J. Blouw, M. Carpinelli, T. Kordich, G. Lynch, W. Hoek, A. Gaidot, P. Manfredi, T. Meyer, D. Wagner, M. Benkebil, A. Valassi, N. Qi, L. Roos, V. Luth, G. Sciolla, G. Monchenault, S. Wagner, J. Olsen, S. Wu, A. Hauke, D. Boutigny, W. Innes, F. Forti, A. Ryd, V. Re, J. Wimmersperg-Toeller, M. Posocco, M. Turri, K. Schubert, V. Brigljevic, G. Michelon, S. George, E. Paoloni, R. Kroeger, R. Bionta, T. Moore, J. Trischuk, S. Petrak, A. Grillo, A. Farbin, C. Angelini, V. Zacek, G. Cowan, J. Beringer, E. Gabathuler, S. Bagnasco, R. Prepost, I. Scott, R. Sloane, D. Bernard, L. Gladney, A. Smol, J. Tinslay, J. Panetta, P. Reinertsen, F. Galeazzi, M. Rotondo, A. Romosan, C. Hast, M. Kocian, M. Serra, R. Hamilton, A. Snyder, F. Jackson, F. Pastore, M. Mugge, M. Zito, R. Seitz, K. Paick, C. Marker, S. Levy, J. Zhang, J. Swain, A. Lu, G. Rong, M. Morganti, A. Roodman, M. Pia, W. Wenzel, J. Lory, H. Lacker, R. Schmitz, A. Telnov, N. Kuznetsova, Y. Skovpen, S. Sekula, A. Calcaterra, J. Chauveau, G. Mancinelli, V. Lillard, S. Jayatilleke, G. Vuagnin, C. Voena, C. Leclerc, I. Scott, B. Abbott, P. Dixon, R. Yamamoto, R. Zhu, P. Robbe, D. Falciai, W. Kozanecki, T. Adye, W. Meyer, H. Quinn, G. London, P. David, B. Spaan, M. Huffer, A. Dorigo, A. Breon, R. Field, J. Nielsen, V. Sharma, G. Vasseur, J. Kadyk, B. Dahmes, L. Behr, A. Eisner, G. Bonneaud, E. Chen, E. Varnes, R. Mount, A. Buzykaev, D. Wagoner, L. Lista, G. Chen, G. Cavoto, G. Nardo, F. Yumiceva, M. Rama, R. Cowan, S. Spanier, A. Zallo, R. Waldi, C. Sutton, R. Contri, N. Savvas, M. Kalelkar, M. Carroll, S. Luitz, M. Grothe, A. Anjomshoaa, G. Vasileiadis, C. Voci, T. Dignan, S. Willocq, M. Kay, R. Kadel, Mike Williams, C. Bozzi, D. Kirkby, T. Geld, A. Eichenbaum, U. Nauenberg, M. Milek, N. Barlow, J. Izen, A. Santroni, M. Bona, S. Ganzhur, F. Martinez-Vidal, R. Jacobsen, M. Booke, A. Buzzo, M. Vetere, Jc Chen, J. Bauer, A. Lutz, M. Perl, G. Rizzo, E. Vallazza, T. Liu, D. Roberts, S. Chun, A. Mckemey, E. Robutti, L. Kerth, A. Kurup, C. Cartaro, M. Pallavicini, G. Wormser, S. Emery, S. Ricciardi, A. Jawahery, J. Kral, B. Schumm, C. Hawkes, S. Versillé, W. Walkowiak, J. Va’vra, L. Bosisio, J. Stark, V. Lepeltier, J. Mcfall, J. McKenna, L. Piemontese, A. Meyer, A. Yushkov, W. Bhimji, G. Batignani, N. Sinev, Y. Zhu, M. Prest, K. McDonald, J. Harton, E. Mały, D. MacFarlane, J. Button‐Shafer, C. Jessop, M. Schune, P. Burchat, P. Bright-Thomas, J. Cohen-Tanugi, N. Neri, F. Simonetto, D. Coward, M. Sullivan, G. Triggiani, P. Leruste, X. Shi, J. Andress, G. Raven, I. Kitayama, D. Strom, D. Coupal, Q. Guo, C. O'grady, M. Green, M. Purohit, W. Ford, S. Xella, D. Muller, K. Flood, Shengxiang Yang, F. Bianchi, T. Gabriel, D. Dorfan, J. Weatherall, M. Mandelkern, D. Thiessen, D. Hitlin, G. Dahlinger, Zongfu Yu, P. Hart, P. Sanders, T. Deppermann, W. Cottingham, R. Henderson, W. Toki, A. Perazzo, J. Allison, I. Peruzzi, C. Lu, S. Dittongo, G. Lafferty, P. Mcgrath, R. Müller-Pfefferkorn, S. Playfer, R. Schwierz, J. Lees, G. Eigen, S. Devmal, T. Handler, R. Johnson, H. Marsiske, J. Gaillard, M. Margoni, O. Hamon, A. Ahsan, C. Patrignani, K. Moffeit, N. Cavallo, Harold Park, M. Verderi, M. Morandin, E. Frank, A. Weinstein, M. Pripstein, S. Kluth, T. Pulliam, S. Serednyakov, E. Treadwell, S. Gowdy, R. Baldini-Ferroli, Sokoloff, M. Levi, S. Laplace, B. Brau, J. Schieck, F. Lodovico, N. Copty, F. Wilson, F. Salvatore, R. Messner, M. Turcotte, J. Brau, F. Anulli, M. Kelsey, A. Smith, V. Shelkov, J. Dorfan, M. Dickopp, R. Sangro, V. Telnov, B. Mayer, S. Christ, S. Prell, S. Jolly, C. Bula, J. Nash, D. Bowerman, P. Vidal, R. Frey, D. Smith, B. Meadows, P. Rankin, Mazzoni, J. Krug, J. Smith, S. O’Neale, J. Johnson, B. Lewandowski, E. Rosenberg, K. Abe, H. Shorthouse, H. Band, V. Miftakov, S. Bettarini, B. Girolamo, B. Stugu, E. Leonardi, P. Kim, A. Hocker, E. Roussot, G. Finocchiaro, S. Metzler, V. Speziali, T. Wenaus, D. Stoker, P. Dauncey, D. Leith, S. Fahey, R. Penny, A. Watson, S. Otto, P. Patel, C. Thiebaux, G. Ricca, A. Seiden, T. Mattison, D. Re, D. Wallom, P. Paolucci, E. Bloom, F. Bulos, S. Sen, D. Azzopardi, D. Aston, S. Farinon, P. Jacques, V. Blinov, P. Giraud, R. Liu, A. Forti, B. Franek, D. Gamba, P. Oddone, J. LoSecco, E. Torassa, R. Parodi, W. Wisniewski, T. Mcmahon, I. Eschrich, J. Schwiening, U. Langenegger, W. Dunwoodie, S. Ferrag, Y. Pan, R. Bernet, L. Lanceri, I. Adam, H. Briand, O. Long, R. Wilson, G. Calderini, B. Ratcliff, L. Cremaldi, G. Gopal, D. Zanin, R. Stroili, A. Lankford, W. Bugg, E. Charles, R. Plano, T. Schietinger, R. Claus, N. Dyce, A. Woch, M. George, A. Khan, P. Anthony, W. Kroeger, C. Wuest, M. Steinke, C. Vaissière, M. Macrì, R. Dubitzky, C. Sciacca, C. Priano, J. Back, A. Samuel, A. Onuchin, N. Gunawardane, H. Staengle, F. Taylor, D. Wright, V. Ivanchenko, F. Colecchia, S. Robertson, R. Gamet, P. Taras, G. Domenico, D. Summers, D. Sanders, R. Aleksan, P. Grosso, B. Serfass, P. Strother, Ping Wang, E. Simopoulos, M. Langer, A. Silva, M. Doser, J. Cochran, A. Pompili, H. Sadrozinski, S. Schaffner, F. Tehrani (2001)
Study of T and CP violation in B0 and anti-B0 mixing with inclusive dileption events
K. Ting, Guang-Tong Zhou, Fei Liu, Swee Tan (2010)
Mass estimation and its applicationsProceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Hao Huang, Hong Qin, Shinjae Yoo, Dantong Yu (2012)
Local anomaly descriptor: a robust unsupervised algorithm for anomaly detection based on diffusion spaceProceedings of the 21st ACM international conference on Information and knowledge management
D. Horn, A. Gottlieb (2001)
Algorithm for data clustering in pattern recognition problems based on quantum mechanics.Physical review letters, 88 1
Hao Huang, Shinjae Yoo, Hong Qin, Dantong Yu (2011)
A Robust Clustering Algorithm Based on Aggregated Heat Kernel Mapping2011 IEEE 11th International Conference on Data Mining
T. Vries, S. Chawla, M. Houle (2010)
Finding Local Anomalies in Very High Dimensional Space2010 IEEE International Conference on Data Mining
Y. Lipman, R. Rustamov, T. Funkhouser (2010)
Biharmonic distanceACM Trans. Graph., 29
A. Statnikov, C. Aliferis, I. Tsamardinos, Douglas Hardin, S. Levy (2004)
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosisBioinformatics, 21 5
M. Weinstein (2009)
Strange Bedfellows: Quantum Mechanics and Data MiningArXiv, abs/0911.0462
B. Nadler, Stéphane Lafon, R. Coifman, I. Kevrekidis (2005)
Diffusion maps, spectral clustering and reaction coordinates of dynamical systemsApplied and Computational Harmonic Analysis, 21
L. Maaten, E. Postma, J. Herik (2008)
Dimensionality Reduction: A Comparative Review
J. Hartigan, M. Wong (1979)
A k-means clustering algorithm
V. Popovici, Weijie Chen, Brandon Gallas, C. Hatzis, W. Shi, F. Samuelson, Y. Nikolsky, M. Tsyganova, A. Ishkin, T. Nikolskaya, K. Hess, V. Valero, D. Booser, M. Delorenzi, M. Delorenzi, G. Hortobagyi, Leming Shi, W. Symmans, L. Pusztai (2010)
Effect of training-sample size and classification difficulty on the accuracy of genomic predictorsBreast Cancer Research : BCR, 12
Ke Zhang, Marcus Hutter, Huidong Jin (2009)
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World DataArXiv, abs/0903.3257
Mathieu Aubry, Ulrich Schlickewei, D. Cremers (2011)
The wave kernel signature: A quantum mechanical approach to shape analysis2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Stéphane Lafon, Y. Keller, R. Coifman (2006)
Data Fusion and Multicue Data Matching by Diffusion MapsIEEE Transactions on Pattern Analysis and Machine Intelligence, 28
D. Kushnir, A. Haddad, R. Coifman (2012)
Anisotropic diffusion on sub-manifolds with application to Earth structure classificationApplied and Computational Harmonic Analysis, 32
B. Pogorelc, M. Gams (2010)
Discovery of Gait Anomalies from Motion Sensor Data2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2
Z. Syed, I. Rubinfeld (2010)
Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes
C. Marzban (2004)
The ROC Curve and the Area under It as Performance MeasuresWeather and Forecasting, 19
U. Luxburg (2007)
A tutorial on spectral clusteringStatistics and Computing, 17
M. Breunig, H. Kriegel, R. Ng, J. Sander (2000)
LOF: identifying density-based local outliers
Hao Huang, Hong Qin, Shinjae Yoo, Dantong Yu (2012)
A New Anomaly Detection Algorithm Based on Quantum Mechanics2012 IEEE 12th International Conference on Data Mining
Isabelle Guyon, Jiwen Li, T. Mader, Patrick Pletscher, Georg Schneider, M. Uhr (2007)
Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmarkPattern Recognit. Lett., 28
Fei Liu, K. Ting, Zhi-Hua Zhou (2008)
Isolation Forest2008 Eighth IEEE International Conference on Data Mining
Jing Gao, H. Cheng, P. Tan (2006)
Semi-supervised outlier detectionProceedings of the 2006 ACM symposium on Applied computing
N. Nasios, A. Bors (2007)
Kernel-based classification using quantum mechanicsPattern Recognit., 40
(2014)
Article 14, Publication date
Xingquan Zhu, Xindong Wu, Chengqi Zhang (2009)
Vague One-Class Learning for Data Streams2009 Ninth IEEE International Conference on Data Mining
J. Richards, P. Freeman, Ann Lee, C. Schafer (2009)
Accurate parameter estimation for star formation history in galaxies using SDSS spectraMonthly Notices of the Royal Astronomical Society, 399
Marc Alexa, Michael Kazhdan, Jian Sun, M. Ovsjanikov, L. Guibas
Eurographics Symposium on Geometry Processing 2009 a Concise and Provably Informative Multi-scale Signature Based on Heat Diffusion
G. Greenstein, A. Zajonc (1997)
The Quantum Challenge: Modern Research on the Foundations of Quantum Mechanics
Keith Noto, C. Brodley, D. Slonim (2010)
Anomaly Detection Using an Ensemble of Feature Models2010 IEEE International Conference on Data Mining
Y. Gliklikh (2011)
Stochastic Analysis on Manifolds
(2013)
Received December
A. Madansky (1988)
Identification of Outliers
Zhi Liu, Qihang Wu, Yun Zhang, C. Chen (2011)
Adaptive least squares support vector machines filter for hand tremor canceling in microsurgeryInternational Journal of Machine Learning and Cybernetics, 2
Amrudin Agovic, A. Banerjee, A. Ganguly, V. Protopopescu (2007)
Anomaly Detection in Transportation Corridors using Manifold Embedding
Daniel Barbará, C. Domeniconi, James Rogers (2006)
Detecting outliers using transduction and statistical testing
Physics-Based Anomaly Detection Defined on Manifold Space HAO HUANG and HONG QIN, Computer Science Department, Stony Brook University SHINJAE YOO and DANTONG YU, Computational Science Center, Brookhaven National Laboratory Current popular anomaly detection algorithms are capable of detecting global anomalies but often fail to distinguish local anomalies from normal instances. Inspired by contemporary physics theory (i.e., heat diffusion and quantum mechanics), we propose two unsupervised anomaly detection algorithms. Building on the embedding manifold derived from heat diffusion, we devise Local Anomaly Descriptor (LAD), which faithfully reveals the intrinsic neighborhood density. It uses a scale-dependent umbrella operator to bridge global and local properties, which makes LAD more informative within an adaptive scope of neighborhood. To offer more stability of local density measurement on scaling parameter tuning, we formulate Fermi Density Descriptor (FDD), which measures the probability of a fermion particle being at a specific location. By choosing the stable energy distribution function, FDD steadily distinguishes anomalies from normal instances with any scaling parameter setting. To further enhance the efficacy of our proposed algorithms, we explore the utility of anisotropic Gaussian kernel (AGK), which offers better manifold-aware affinity information. We also quantify and examine the effect of different Laplacian normalizations
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Sep 23, 2014
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