Incremental multiple instance outlier detection

AbstractI-MLOF algorithm is an extension of local outlier factor (LOF) algorithm in multiple instance (MI) setting. The task of I-MLOF is to identify MI outlier. However, I-MLOF algorithm works in batch mode, where all samples must be provided for once. In some real applications such as industrial detection and traffic monitoring, MI outlier is required to be identified from data stream. The batch-mode outlier detection methods usually cannot be applied directly to these applications. In this paper, an incremental MI outlier detection algorithm “Inc I-MLOF” is proposed. MI outlier detection can be done for sequentially arrived data with Inc I-MLOF. We prove theoretically that Inc I-MLOF achieves the equal result to that of I-MLOF. The experimental results illustrate Inc I-MLOF achieves good performance on several synthetic and real data sets.

[1]  Frédo Durand,et al.  Patch Complexity, Finite Pixel Correlations and Optimal Denoising , 2012, ECCV.

[2]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[3]  Hans-Peter Kriegel,et al.  Angle-based outlier detection in high-dimensional data , 2008, KDD.

[4]  Joseph F. Murray,et al.  Improved disk-drive failure warnings , 2002, IEEE Trans. Reliab..

[5]  Feiping Nie,et al.  Multiple rank multi-linear SVM for matrix data classification , 2014, Pattern Recognit..

[6]  Cornelius Held,et al.  Intelligent Video Surveillance , 2012, Computer.

[7]  Jun Gao,et al.  Identifying Multi-instance Outliers , 2010, SDM.

[8]  Aleksandar Lazarevic,et al.  Incremental Local Outlier Detection for Data Streams , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[9]  Feiping Nie,et al.  Maximum Margin Multi-Instance Learning , 2011, NIPS.

[10]  Michael R. Anderson,et al.  Robust Image Denoising with Multi-Column Deep Neural Networks , 2013, NIPS 2013.

[11]  William Perrizo,et al.  RDF: a density-based outlier detection method using vertical data representation , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[12]  Alberto Muñoz,et al.  Self-organizing maps for outlier detection , 1998, Neurocomputing.

[13]  Feiping Nie,et al.  Learning Instance Specific Distance for Multi-Instance Classification , 2011, AAAI.

[14]  Marco La Cascia,et al.  Path Modeling and Retrieval in Distributed Video Surveillance Databases , 2012, IEEE Transactions on Multimedia.

[15]  Shin Ando,et al.  Clustering Needles in a Haystack: An Information Theoretic Analysis of Minority and Outlier Detection , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[16]  Jae-Gil Lee,et al.  Temporal Outlier Detection in Vehicle Traffic Data , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[17]  Yi Wu,et al.  Stable local dimensionality reduction approaches , 2009, Pattern Recognit..

[18]  Jae-Gil Lee,et al.  Trajectory Outlier Detection: A Partition-and-Detect Framework , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[19]  Dragoljub Pokrajac,et al.  Spherical coverage verification , 2011, Appl. Math. Comput..

[20]  Billy M. Williams,et al.  MODELING AND FORECASTING VEHICULAR TRAFFIC FLOW AS A SEASONAL STOCHASTIC TIME SERIES PROCESS , 1999 .

[21]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[22]  Joseph F. Murray,et al.  Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application , 2005, J. Mach. Learn. Res..

[23]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD 2000.

[24]  Feiping Nie,et al.  Multiple view semi-supervised dimensionality reduction , 2010, Pattern Recognit..

[25]  Philip S. Yu,et al.  Exploiting Local Data Uncertainty to Boost Global Outlier Detection , 2010, 2010 IEEE International Conference on Data Mining.

[26]  Chang-Tien Lu,et al.  Spatial Weighted Outlier Detection , 2006, SDM.

[27]  Anders Heyden,et al.  Outlier detection in video sequences under affine projection , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[28]  Takafumi Kanamori,et al.  Inlier-Based Outlier Detection via Direct Density Ratio Estimation , 2008, 2008 Eighth IEEE International Conference on Data Mining.