Algorithms for clustering data
Algorithms for clustering data
Covariance Matrix Estimation and Classification With Limited Training Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
ML estimation of the multivariate t distribution and the EM algorithm
Journal of Multivariate Analysis
GTM: the generative topographic mapping
Neural Computation
A procedure for the detection of multivariate outliers
Computational Statistics & Data Analysis
Self-organization of shift-invariant receptive fields
Neural Networks
Adaptive Filters: Theory and Applications
Adaptive Filters: Theory and Applications
Robust mixture modelling using the t distribution
Statistics and Computing
Kernel-based topographic map formation by local density modeling
Neural Computation
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Class distribution on SOM surfaces for feature extraction and object retrieval
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Maximum Likelihood Topographic Map Formation
Neural Computation
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
3D head model retrieval in kernel feature space using HSOM
Pattern Recognition
A linear fit gets the correct monotonicity directions
Machine Learning
Extracting a diagnostic gait signature
Pattern Recognition
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
Chaotic Time Series Prediction Using a Neuro-Fuzzy System with Time-Delay Coordinates
IEEE Transactions on Knowledge and Data Engineering
Selecting the right MBA schools - An application of self-organizing map networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Visualizing the evolution of a web-based social network
Journal of Network and Computer Applications
Robust fuzzy clustering using mixtures of Student's-t distributions
Pattern Recognition Letters
Heart sound classification using wavelet transform and incremental self-organizing map
Digital Signal Processing
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Robust Bayesian mixture modelling
Neurocomputing
Self-organizing mixture models
Neurocomputing
Self-organizing mixture networks for probability density estimation
IEEE Transactions on Neural Networks
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Derivation of a class of training algorithms
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Incremental model selection and ensemble prediction under virtual concept drifting environments
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Probabilistic self-organizing maps for qualitative data
Neural Networks
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Stochastic approximation for background modelling
Computer Vision and Image Understanding
Reduction of JPEG compression artifacts by kernel regression and probabilistic self-organizing maps
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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The original Kohonen's Self-Organizing Map model has been extended by several authors to incorporate an underlying probability distribution. These proposals assume mixtures of Gaussian probability densities. Here we present a new self-organizing model which is based on a mixture of multivariate Student-t components. This improves the robustness of the map against outliers, while it includes the Gaussians as a limit case. It is based on the stochastic approximation framework. The 'degrees of freedom' parameter for each mixture component is estimated within the learning procedure. Hence it does not need to be tuned manually. Experimental results are presented to show the behavior of our proposal in presence of outliers, and its performance in adaptive filtering and classification problems.