Feature-based clustering for electricity use time series data
T Räsänen, M Kolehmainen - … , ICANNGA 2009, Kuopio, Finland, April 23 …, 2009 - Springer
Time series clustering has been shown effective in providing useful information in various
applications. This paper presents an efficient computational method for time series …
applications. This paper presents an efficient computational method for time series …
A survey of feature selection methods for Gaussian mixture models and hidden Markov models
Feature selection is the process of reducing the number of collected features to a relevant
subset of features and is often used to combat the curse of dimensionality. This paper …
subset of features and is often used to combat the curse of dimensionality. This paper …
AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations
Background Atrial fibrillation (AF) is the most common heart arrhythmia worldwide and is
linked to a higher risk of mortality and morbidity. To predict AF and AF-related complications …
linked to a higher risk of mortality and morbidity. To predict AF and AF-related complications …
Metrics for probabilistic geometries
We investigate the geometrical structure of probabilistic generative dimensionality reduction
models using the tools of Riemannian geometry. We explicitly define a distribution over the …
models using the tools of Riemannian geometry. We explicitly define a distribution over the …
Development of a patent roadmap through the Generative Topographic Mapping and Bass diffusion model
This paper aims to present a novel concept roadmap—the patent roadmap—and suggest an
advanced patent roadmapping process, based on the Generative Topographic Mapping …
advanced patent roadmapping process, based on the Generative Topographic Mapping …
Change point detection in time series data with random forests
A large class of monitoring problems can be cast as the detection of a change in the
parameters of a static or dynamic system, based on the effects of these changes on one or …
parameters of a static or dynamic system, based on the effects of these changes on one or …
Odor recognition in robotics applications by discriminative time-series modeling
Odor classification by a robot equipped with an electronic nose (e-nose) is a challenging
task for pattern recognition since volatiles have to be classified quickly and reliably even in …
task for pattern recognition since volatiles have to be classified quickly and reliably even in …
Probabilistic self-organizing maps for continuous data
E López-Rubio - IEEE Transactions on Neural Networks, 2010 - ieeexplore.ieee.org
The original self-organizing feature map did not define any probability distribution on the
input space. However, the advantages of introducing probabilistic methodologies into self …
input space. However, the advantages of introducing probabilistic methodologies into self …
[PDF][PDF] Kernel generative topographic mapping.
Kernel Generative Topographic Mapping Page 1 Kernel Generative Topographic Mapping Iván
Olier 1 , Alfredo Vellido 2 and Jesús Giraldo 3,1 * 1- Institut de Neurosci`ences, and 3- Unitat de …
Olier 1 , Alfredo Vellido 2 and Jesús Giraldo 3,1 * 1- Institut de Neurosci`ences, and 3- Unitat de …
Mapping the global free expression landscape using machine learning
Freedom of expression is a core human right, yet the forces that seek to suppress it have
intensified, increasing the need to develop tools that can measure the rates of freedom …
intensified, increasing the need to develop tools that can measure the rates of freedom …