Model-Coupled Autoencoder for Time Series Visualisation

Information theory and statistics

Solomon Kullback · 1959

Earlier work this paper cites.

Nonlinear principal component analysis using autoassociative neural networks

M. A. Kramer · 1991

Earlier work this paper cites.

Magnification factors for the GTM algorithm

Christopher Bishop, Markus Svensen, and Christopher K. I. Williams · 1997

Earlier work this paper cites.

Exploiting generative models in discriminative classifiers

T. Jaakkola and D. Haussler · 1998

Earlier work this paper cites.

A model-independent analysis of the variability of GRS 1915+105

T. Belloni, M. Klein-Wolt, M. Méndez, M. van der Klis, and J. van Paradijs · 2000

Earlier work this paper cites.

Stochastic models that separate fractal dimension and the hurst effect

T. Gneiting and M. Schlather · 2004

Earlier work this paper cites.

Probability product kernels

T. Jebara, R. Kondor, and A. Howard · 2004

Earlier work this paper cites.

Probabilistic non-linear principal component analysis with gaussian process latent variable models

Neil D. Lawrence · 2005

Cited alongside, same era.

Pattern Recognition and Machine Learning

Christopher Bishop · 2006

Cited alongside, same era.

Visualizing data using t-SNE

Laurens van der Maaten and Geoffrey Hinton · 2008

Cited alongside, same era.

Reservoir computing approaches to recurrent neural network training

Mantas Lukosevicius and Herbert Jaeger · 2009

Cited alongside, same era.

Echo state gaussian process

S.P. Chatzis and Y. Demiris · 2011

Cited alongside, same era.

Minimum complexity echo state network

A. Rodan and P. Tino · 2011

Cited alongside, same era.

Comparative study of visualisation methods for temporal data

Tzai-Der Wang, Xiaochuan Wu, and Colin Fyfe · 2012

Later among the works it cites.

Model-based kernel for efficient time series analysis

H. Chen, F. Tang, P. Tino, and X. Yao · 2013

Later among the works it cites.

Linear autoencoder networks for structured data

Alessandro Sperduti · 2013

Later among the works it cites.

Learning in the model space for cognitive fault diagnosis

H. Chen, P. Tino, A. Rodan, and X. Yao · 2014

Later among the works it cites.

Autoencoding time series for visualisation

N. Gianniotis, S.D. Kuegler, and R. Misra P. Tino, K. Polsterer · 2015

Later among the works it cites.

Scalable classification of repetitive time series through frequencies of local polynomials

J. Grabocka, M. Wistuba, and L. Schmidt-Thieme · 2015

Later among the works it cites.