Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation
By Yoshinobu Kawahara, Masashi Sugiyama. 2009
214 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Qiang Yang
- Sinno Jialin Pan
- Ivor Wai-Hung Tsang
- Mingsheng Long
- Jianmin Wang
- Bernhard Schölkopf
- Wouter M. Kouw
- Jialin Pan
In Molecular Biology
- Qiang Yang
- Jialin Pan
- Ivor Wai-Hung Tsang
- Nicolas Vayatis
- Laurent Oudre
- Charles Truong
- James T. Kwok
- Eyke Hüllermeier
Citations in the corpus
- Domain Adaptation via Transfer Component Analysis (2009, background): “MMDE aims at learning a shared latent space underlying the domains where distance between distributions can be reduced while the data variance can be preserved.”
- Open challenges for data stream mining research (2014, methodology): “Hence, how should entity drift be traced, and how should the interplay between entity drift and model drift be captured? In the unsupervised setting, Oliveira and Gama learn and monitor clusters as states of evolution [32], while [41] extend that work to learn Markov chains that mark the entities’ e”
- Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation (2012, background): “2011a) change detection in time series (Kawahara and Sugiyama 2009), conditional density estimation (Sugiyama et al.”
- Feature-based transfer learning with real-world applications (2010, methodology): “To be emphasized that besides covariate shift adaptation, these importance estimation techniques have also been applied to various applications, such as independent component analysis (ICA) [181], outlier detection [78] and change-point detection [92].”
- A review of change point detection methods (2018, methodology): “In the context of single change point detection, examples of such tests include rankbased procedures [38, 94, 97], kernel Fisher discriminants [62, 63, 103], density ratio [74], etc.”
- Constructive setting for problems of density ratio estimation (2015, background): “The estimation of the ratio of two probability densities from a given collection of data is a fundamental technique for solving many data mining problems, including data adaptation [1,2], conditional probability and regression estimation [3], estimation of mutual information [4], change detection in”