Kernel Change-point Analysis

By Zaïd Harchaoui, Francis R. Bach, Eric Moulines. 2008

223 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “40) coincides with that of classical set kernel of Haussler (1999) and Gärtner et al.”
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response (2013, methodology): “Similarly, change point detection methods [40], [41] perform a sequence of change-point analysis in a sliding window along the time dimension.”
Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion (2013, background): “In particular, there is a substantial amount of work in the field of data mining [27], [28], speech processing [29], [30], animal behavior analysis [12], [31], and signal processing [32], [33].”
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation (2012, methodology): “Following the strategy of the previous researches (Desobry et al., 2005; Harchaoui et al., 2009), peaks of a change-point score are regarded as detection alarms.”
Maximum Margin Temporal Clustering (2012, methodology): “Change point detection such as Xuan and Murphy (2007) and Harchaoui et al. (2009) is another popular time series segmentation technique; it works by performing a sequence of change-point analysis in a sliding window along the time dimension.”
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, background): “Recent work has proposed new distance measures between high-dimensional multivariate probability distributions, such as the Wasserstein distance, f-divergences 42 , and kernel-based measures 43,44 .”
Structured Time Series Analysis for Human Action Segmentation and Recognition (2014, methodology): “28 is set to be 0.5 and results (Table 2) show that our approach only misclassifies 5% sequences, or 1.2% by weighing with the number of frames.”
Selective review of offline change point detection methods (2020, unknown): “It should be noted that certain window-based detection methods in the literature rely on a discrepancy measure which is not related to a cost function, as in (24) [11, 121–123].”
A Survey of Methods for Time Series Change Point Detection (2016, unknown): “Method Dataset 1 [2] [22] Dataset 2 [2][63][50] Dataset 3 [58] Dataset 4 [24] Dataset 5 [26] Dataset 6 [21] Dataset 7 [22]”
A review of change point detection methods (2018, background): “Change point detection can be performed on a high-dimensional mapping of the original signal, implicitly defined by a kernel function [4, 42, 59, 62].”
Category-Specific Video Summarization (2014, unknown): “Change-point detection has been subject of intense theoretical and methodological study in statistics and signal processing; see [26,27] and references therein.”
Homogeneity and change-point detection tests for multivariate data using rank statistics (2011, methodology): “06), which is a kernel-based test here used with a Gaussian kernel having a bandwidth given by the median distance between the samples as suggested by Gretton et al. (2006), Désobry et al. (2005) and Harchaoui et al. (2008). The second approach is the classical Hotelling’s T2-test (Chen and Gupta, 2”