Open AccessPosted Content
Kernel change-point detection
TL;DR: The proposed penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Cappe (2007) is proposed and it is proved it satisfies a non-asymptotic oracle inequality by showing a new concentration result in Hilbert spaces.
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Abstract: We tackle the change-point problem with data belonging to a general set We propose a penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Cappe (2007) This penalty generalizes the one proposed for one dimensional signals by Lebarbier (2005) We prove it satisfies a non-asymptotic oracle inequality by showing a new concentration result in Hilbert spaces Experiments on synthetic and real data illustrate the accuracy of our method, showing it can detect changes in the whole distribution of data, even when the mean and variance are constant Our algorithm can also deal with data of complex nature, such as the GIST descriptors which are commonly used for video temporal segmentation
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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
A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
TL;DR: The divisive method is shown to provide consistent estimates of both the number and the location of change points under standard regularity assumptions, and methods from cluster analysis are applied to assess performance and to allow simple comparisons of location estimates, even when the estimated number differs.
Category-Specific Video Summarization
Danila Potapov,Matthijs Douze,Zaid Harchaoui,Cordelia Schmid +3 more
- 06 Sep 2014
TL;DR: In large video collections with clusters of typical categories, such as “birthday party” or “flash-mob”, category-specific video summarization can produce higher quality video summaries than unsupervised approaches that are blind to the video category.
Multiscale change point inference
TL;DR: A new estimator, the simultaneous multiscale change point estimator SMUCE, is introduced, which achieves the optimal detection rate of vanishing signals as n→∞, even for an unbounded number of change points.
423
•Posted Content
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.
321
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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
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•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.