Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series
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[1] Kenji Yamanishi,et al. A unifying framework for detecting outliers and change points from time series , 2006, IEEE Transactions on Knowledge and Data Engineering.
[2] Noga Alon,et al. The Space Complexity of Approximating the Frequency Moments , 1999 .
[3] Olivier Ledoit,et al. A well-conditioned estimator for large-dimensional covariance matrices , 2004 .
[4] Jilles Vreeken,et al. Non-parametric Jensen-Shannon Divergence , 2015, ECML/PKDD.
[5] Per-Gunnar Martinsson,et al. Randomized algorithms for the low-rank approximation of matrices , 2007, Proceedings of the National Academy of Sciences.
[6] Davide Anguita,et al. Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions , 2014, ICANN.
[7] Robert E. Tarjan,et al. Fibonacci heaps and their uses in improved network optimization algorithms , 1984, JACM.
[8] Edo Liberty,et al. Simple and deterministic matrix sketching , 2012, KDD.
[9] D. W. Scott,et al. Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .
[10] Manuel Davy,et al. An online kernel change detection algorithm , 2005, IEEE Transactions on Signal Processing.
[11] Xiangliang Zhang,et al. A PCA-Based Change Detection Framework for Multidimensional Data Streams: Change Detection in Multidimensional Data Streams , 2015, KDD.
[12] Saeed Amizadeh,et al. Generic and Scalable Framework for Automated Time-series Anomaly Detection , 2015, KDD.
[13] José Carlos Príncipe,et al. A Unified Framework for Quadratic Measures of Independence , 2011, IEEE Transactions on Signal Processing.
[14] Masashi Sugiyama,et al. Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation , 2009, SDM.
[15] Suresh Venkatasubramanian,et al. Change (Detection) You Can Believe in: Finding Distributional Shifts in Data Streams , 2009, IDA.
[16] Ludmila I. Kuncheva,et al. PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[17] W. Marsden. I and J , 2012 .
[18] Charu C. Aggarwal. A Framework for Change Diagnosis of Data Streams. , 2003, SIGMOD 2003.
[19] Sanjay Ranka,et al. Statistical change detection for multi-dimensional data , 2007, KDD '07.
[20] Kuldip K. Paliwal,et al. Fast principal component analysis using fixed-point algorithm , 2007, Pattern Recognit. Lett..
[21] Tamás Sarlós,et al. Improved Approximation Algorithms for Large Matrices via Random Projections , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).
[22] Shai Ben-David,et al. Detecting Change in Data Streams , 2004, VLDB.
[23] Zaïd Harchaoui,et al. Kernel Change-point Analysis , 2008, NIPS.
[24] Masashi Sugiyama,et al. Change-point detection in time-series data by relative density-ratio estimation , 2012 .
[25] Nathan Halko,et al. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..
[26] Uwe Krüger,et al. Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment , 2013, ICML.
[27] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[28] Jessika Weiss,et al. Graphical Models In Applied Multivariate Statistics , 2016 .
[29] Ricard Gavaldà,et al. Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.
[30] Michael I. Jordan. Graphical Models , 1998 .
[31] Lawrence K. Saul,et al. Identifying suspicious URLs: an application of large-scale online learning , 2009, ICML '09.
[32] Michael I. Jordan,et al. Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..
[33] Klemens Böhm,et al. Unsupervised interaction-preserving discretization of multivariate data , 2014, Data Mining and Knowledge Discovery.
[34] Petros Drineas,et al. Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix , 2006, SIAM J. Comput..
[35] Klemens Böhm,et al. 4S: Scalable subspace search scheme overcoming traditional Apriori processing , 2013, 2013 IEEE International Conference on Big Data.
[36] Michel Verleysen,et al. Nonlinear Dimensionality Reduction , 2021, Computer Vision.