Fetching the paper…
Reading the bibliography…
The Mahalanobis distance is a classical tool used to measure the covariance-adjusted distance between points in $\bbR^d$.
“On the generalized distance in statistics”
Prasanta Mahalanobis · 1936
Earlier work this paper cites.
“Nonlinear Problems in Random Theory”
Norbert Wiener · 1958
Earlier work this paper cites.
“Series expansions for nonlinear systems”
I Sandberg · 1983
Earlier work this paper cites.
“Fading memory and the problem of approximating nonlinear operators with Volterra series”
S. Boyd and L. Chua · 1985
Earlier work this paper cites.
“Probability distributions on Banach spaces”, Mathematics and its Applications
N..;V..;S.. Chobanyan · 1987
Earlier work this paper cites.
“Probability in Banach Spaces”
Michel Ledoux and Michel Talagrand · 1991
Earlier work this paper cites.
“Localization: theory and experiment”
B Kramer and A MacKinnon · 1993
Earlier work this paper cites.
“Subgaussian random variables in Hilbert spaces”
Rita Antonini · 1997
Earlier work this paper cites.
“Matrix Analysis”
Rajendra Bhatia · 1997
Earlier work this paper cites.
“Differential equations driven by rough signals.”
Terry. Lyons · 1998
Earlier work this paper cites.
“Nonlinear Component Analysis as a Kernel Eigenvalue Problem”
Bernhard Schölkopf, Alexander Smola and Klaus-Robert Müller · 1998
Earlier work this paper cites.
“The Mahalanobis distance”
R. De Maesschalck, D. Jouan-Rimbaud and D.L. Massart · 2000
Earlier work this paper cites.
“On the mathematical foundations of learning”
Felipe Cucker and Stephen Smale · 2001
Earlier work this paper cites.
“Nonlinear kernel-based statistical pattern analysis”
A Ruiz and P López-de-Teruel · 2001
Earlier work this paper cites.
“Dynamic Time-Alignment Kernel in Support Vector Machine”
Hiroshi Shimodaira, Ken-ichi Noma, Mitsuru Nakai and Shigeki Sagayama · 2001
Earlier work this paper cites.
“Real-time computing without stable states: a new framework for neural computation based on perturbations”
W. Maass, T. Natschläger and H. Markram · 2002
Earlier work this paper cites.
“Outlier detection using k-nearest neighbour graph”
V. Hautamaki, I. Karkkainen and P. Franti · 2004
Earlier work this paper cites.
“Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication”
Herbert Jaeger and Harald Haas · 2004
Earlier work this paper cites.
“Functional Data Analysis”
J.. Ramsay and B.. Silverman · 2005
Earlier work this paper cites.
“Infinite Dimensional Analysis: A Hitchhiker’s Guide”
C.D. Aliprantis and K.C. Border · 2007
Earlier work this paper cites.
“A Kernel for Time Series Based on Global Alignments”
Marco Cuturi, Jean-Philippe Vert, Oystein Birkenes and Tomoko Matsui · 2007
Earlier work this paper cites.
“Learning Theory: An Approximation Theory Viewpoint”, Cambridge Monographs on Applied and Computational Mathematics
Felipe Cucker and Ding Zhou · 2007
Earlier work this paper cites.
“Stable Rank and Intrinsic Dimension of Real and Complex Matrices”
Ilse.. Ipsen and Arvind. Saibaba · 2007
Earlier work this paper cites.
“Weighted Mahalanobis Distance Kernels for Support Vector Machines”
Defeng Wang, Daniel. Yeung and Eric.. Tsang · 2007
Earlier work this paper cites.
“Learning a Mahalanobis distance metric for data clustering and classification”
Shiming Xiang, Feiping Nie and Changshui Zhang · 2008
Earlier work this paper cites.
“Kernel discriminant analysis for positive definite and indefinite kernels”
Elzbieta Pekalska and Bernard Haasdonk · 2009
Earlier work this paper cites.
“Learning with kernels: support vector machines, regularization, optimization and beyond”, Adaptive computation and machine learning
Alexander Schölkopf Bernhard;Smola · 2009
Cited alongside, same era.
“Detecting anomalies in unmanned vehicles using the Mahalanobis distance”
Raz Lin, Eliyahu Khalastchi and Gal. Kaminka · 2010
Cited alongside, same era.
“Fast global alignment kernels”
Marco Cuturi · 2011
Cited alongside, same era.
“Adaptive Mahalanobis Distance and k k -Nearest Neighbor Rule for Fault Detection in Semiconductor Manufacturing”
Ghislain Verdier and Ariane Ferreira · 2011
Cited alongside, same era.
“The application of rough set and Mahalanobis distance to enhance the quality of OSA diagnosis”
Pa-Chun Wang, Chao-Ton Su, Kun-Huang Chen and Ning-Hung Chen · 2011
Cited alongside, same era.
“Basic Classes of Linear Operators”
“Dimension reduction in recurrent networks by canonicalization”
Lyudmila Grigoryeva and Juan-Pablo Ortega · 2021
Later among the works it cites.
“The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances”
Alejandro Ruiz et al · 2021
Later among the works it cites.
“The Signature Kernel Is the Solution of a Goursat PDE”
Cristopher Salvi et al · 2021
Later among the works it cites.
“Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes”
Cristopher Salvi et al · 2021
Later among the works it cites.
“Applications of Signature Methods to Market Anomaly Detection”
Erdinc Akyildirim, Matteo Gambara, Josef Teichmann and Syang Zhou · 2022
Later among the works it cites.
“Mahalanobis Distance Based K-Means Clustering”
Paul. Brown et al · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
I. Gohberg, S. Goldberg and M. Kaashoek · 2012
Cited alongside, same era.
“Lectures on Gaussian Processes”, Graduate Texts in Mathematics
Mikhail Lifshits · 2012
Cited alongside, same era.
“Introduction to the non-asymptotic analysis of random matrices”
Roman Vershynin · 2012
Cited alongside, same era.
“Linear Integral Equations”, Applied Mathematical Sciences
R. Kress · 2013
Cited alongside, same era.
“Shape outlier detection and visualization for functional data: the outliergram”
Ana Arribas-Gil and Juan Romo · 2014
Cited alongside, same era.
“Functional Analysis”, Pure and Applied Mathematics: A Wiley Series of Texts, Monographs and Tracts
P.D. Lax · 2014
Cited alongside, same era.
“Gaussian Measures”, Mathematical Surveys and Monographs
V.I. Bogachev · 2015
Cited alongside, same era.
Later among the works it cites.
“Discrete-time signatures and randomness in reservoir computing”
Christa Cuchiero et al · 2022
Later among the works it cites.
“Reservoir kernels and Volterra series”
Lukas Gonon, Lyudmila Grigoryeva and Juan-Pablo Ortega · 2022
Later among the works it cites.
“A scale-dependent measure of system dimensionality”
Stefano Recanatesi et al · 2022
Later among the works it cites.
“Randomized Signature Methods in Optimal Portfolio Selection”
Erdinc Akyildirim, Matteo Gambara, Josef Teichmann and Syang Zhou · 2023
Later among the works it cites.
“Neural signature kernels as infinite-width-depth-limits of controlled ResNets”
Nicolaça Cirone, Maud Lemercier and Cristopher Salvi · 2023
Later among the works it cites.
“On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics”
Enea Compagnoni et al · 2023
Later among the works it cites.
“An Introduction to Stochastic PDEs”, 2023
Martin Hairer · 2023
Later among the works it cites.
“PCF-GAN: generating sequential data via the characteristic function of measures on the path space”
Hang Lou, Siran Li and Hao Ni · 2023
Later among the works it cites.
“The Signature Kernel”
Darrick Lee and Harald Oberhauser · 2023
Later among the works it cites.
“G-Signatures: Global Graph Propagation With Randomized Signatures”
Bernhard Schäfl, Lukas Gruber, Johannes Brandstetter and Sepp Hochreiter · 2023
Later among the works it cites.
“Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path Signature”
Zhen Shao et al · 2023
Later among the works it cites.
“Random Fourier Signature Features”
Csaba Toth, Harald Oberhauser and Zoltan Szabo · 2023
Later among the works it cites.
“Novelty Detection on Radio Astronomy Data using Signatures”
Paola Arrubarrena et al · 2024
Closest in time.
“Universal randomised signatures for generative time series modelling”
Francesca Biagini, Lukas Gonon and Niklas Walter · 2024
Closest in time.
“Theoretical Foundations of Deep Selective State-Space Models”
Nicola Cirone et al · 2024
Closest in time.
“Signature Methods in Stochastic Portfolio Theory”
Christa Cuchiero and Janka Möller · 2024
Closest in time.
“Lecture notes on rough paths and applications to machine learning”
Thomas Cass and Cristopher Salvi · 2024
Closest in time.
“Free probability, path developments and signature kernels as universal scaling limits”
Thomas Cass and William. Turner · 2024
Closest in time.
“Path Development Network with Finite-dimensional Lie Group”
Hang Lou, Siran Li and Hao Ni · 2024
Closest in time.