Add to ORKGThe standard GTM (generative topographic mapping) algorithm assumes that the data on which it is trained consists of independent, identically distributed (iid) vectors. For time series, however, the iid assumption is a poor approximation. In this paper we show how the GTM algorithm can be extended to model time series by incorporating it as the emission density in a hidden Markov model. Since GTM has discrete hidden states we are able to find a tractable EM algorithm, based on the forward-backward algorithm, to train the model. We illustrate the performance of GTM through time using flight recorder data from a helicopter
latent variable model that, in its original version, was conceived to provide clustering and visuali...
<p>The analysis of time series and sequences has been challenging in both statistics and machine lea...
The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput....
The standard GTM (generative topographic mapping) algorithm assumes that the data on which it is tra...
Latent variable models represent the probability density of data in a space of several dimensions in...
The Self-Organizing Map (SOM) algorithm has been extensively studied and has been applied with consi...
Generative Topographic Mapping (GTM) is a latent variable model that, in its standard version, was c...
Generative Topographic Mapping (GTM) is a latent variable model that, in its original version, was c...
Accepted for publication in Neural Computation. Latent variable models represent the probability den...
The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if n...
The Self-OrganizingMap (SOM) algorithm has been extensively studied and has been applied with consid...
The Generative Topographic Mapping (GTM) algorithm of Bishop et al. (1997) has been introduced as a ...
We describe the "wake-sleep'' algorithm that allows a multilayer, unsupervised, stochastic neural ne...
Time-dependent natural phenomena and artificial processes can often be quantitatively expressed as m...
This A lot of machine learning concerns with creating statistical parameterized models of systems ba...
latent variable model that, in its original version, was conceived to provide clustering and visuali...
<p>The analysis of time series and sequences has been challenging in both statistics and machine lea...
The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput....
The standard GTM (generative topographic mapping) algorithm assumes that the data on which it is tra...
Latent variable models represent the probability density of data in a space of several dimensions in...
The Self-Organizing Map (SOM) algorithm has been extensively studied and has been applied with consi...
Generative Topographic Mapping (GTM) is a latent variable model that, in its standard version, was c...
Generative Topographic Mapping (GTM) is a latent variable model that, in its original version, was c...
Accepted for publication in Neural Computation. Latent variable models represent the probability den...
The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if n...
The Self-OrganizingMap (SOM) algorithm has been extensively studied and has been applied with consid...
The Generative Topographic Mapping (GTM) algorithm of Bishop et al. (1997) has been introduced as a ...
We describe the "wake-sleep'' algorithm that allows a multilayer, unsupervised, stochastic neural ne...
Time-dependent natural phenomena and artificial processes can often be quantitatively expressed as m...
This A lot of machine learning concerns with creating statistical parameterized models of systems ba...
latent variable model that, in its original version, was conceived to provide clustering and visuali...
<p>The analysis of time series and sequences has been challenging in both statistics and machine lea...
The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput....