Fast Global Alignment Kernels

By Marco Cuturi. 2011

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Citations in the corpus

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting (2019, methodology): “The training set consists of 25, 968 time-points and the task is to perform rolling validation for the next 7 days (24 time-points at a time, for 7 windows) as done in [29, 20, 9];(ii) traffic [7] - Hourly traffic on 963 roads in San Francisco.”
Domain-Adaptive Discriminative One-Shot Learning of Gestures (2014, methodology): “Our contributions are: (i) a method for learning gestures accurately and discriminatively from a single positive training example (in the spirit of one-shot learning and exemplar SVMs/LDAs [21, 25]); (ii) a domain adaptation method for human pose and hand shape that enables generalisation to new dom”
HAck: A system for the recognition of human actions by kernels of visual strings (2014, methodology): “In [5] it is suggested that the best way to set T is to choose a reasonable multiple of the median length, e.”
The UEA multivariate time series classification archive, 2018 (2018, methodology): “This is a UCI dataset from the California Department of Transportation 19 re-ported in [25].”
Data Augmentation for Time Series Classification using Convolutional Neural Networks (2016, methodology): “In [7], Cuturi derived Global Alignment Kernel (GAK) from DTW. GAK is a robust similarity measure that can be used within kernel methods.”
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2020, methodology): “Figure 17 shows the CIF temporal importance curves for the PEMS-SF data.”
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data (2017, methodology): “• DTW, GAK — Dynamic time warping (DTW)-based clustering using a global alignment kernel [9, 42].”
Soft-DTW: a Differentiable Loss Function for Time-Series (2017, methodology): “Because soft-DTW can be used with kernel machines, one typically observes an increase in performance when using soft-DTW over DTW (Cuturi, 2011) for classiﬁcation.”