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A kernel multiple change-point algorithm via model selection
TL;DR: In this paper, a penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Capp{\'e} was proposed.
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Abstract: We tackle the change-point problem with data belonging to a general set. We build a penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Capp{\'e} (2007). This penalty generalizes the one proposed by Lebarbier (2005) for one-dimensional signals. We prove a non-asymptotic oracle inequality for the proposed method, thanks to a new concentration result for some function of Hilbert-space valued random variables. Experiments on synthetic data illustrate the accuracy of our method, showing that it can detect changes in the whole distribution of data, even when the mean and variance are constant.
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Citations
Selective review of offline change point detection methods
TL;DR: In this article, the authors present a selective survey of algorithms for the offline detection of multiple change points in multivariate time series, and a general yet structuring methodological strategy is adopted to organize this vast body of work.
953
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A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
TL;DR: In this paper, a hierarchical clustering approach is proposed to estimate both the number and location of change points in a set of multivariate observations of arbitrary dimension and the positions at which they occur.
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Nonparametric maximum likelihood approach to multiple change-point problems
TL;DR: In this article, the authors proposed a nonparametric maximum likelihood approach to detect multiple change-points in the data sequence, which does not impose any parametric assumption on the underlying distributions.
158
Sequential (Quickest) Change Detection: Classical Results and New Directions
Liyan Xie,Shaofeng Zou,Yao Xie,Venugopal V. Veeravalli +3 more
- 13 Apr 2021
TL;DR: Some new dimensions that emerge at the intersection of sequential change detection with other areas are discussed, along with a selection of modern applications and remarks on open questions.
133
•Posted Content
Large sample analysis of the median heuristic
TL;DR: In theory, this paper provides a convergence analysis that shows the asymptotic normality of the bandwidth chosen by the median heuristic in the setting of kernel two-sample test.
133
References
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Bernhard Schölkopf,Alexander J. Smola +1 more
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TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
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Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Aude Oliva,Antonio Torralba +1 more
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
7.5K
•Book
Kernel Methods for Pattern Analysis
John Shawe-Taylor,Nello Cristianini +1 more
- 01 Jan 2004
TL;DR: This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.