Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Latent Variable Model for Data Visualization
IEEE Transactions on Pattern Analysis and Machine Intelligence
GTM: the generative topographic mapping
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Self-organization of shift-invariant receptive fields
Neural Networks
Faithful representations with topographic maps
Neural Networks
Kernel-based topographic map formation by local density modeling
Neural Computation
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Image compression using self-organizing maps
Systems Analysis Modelling Simulation - Special issue: Digital signal processing and control
Distance-Preserving Projection of High-Dimensional Data for Nonlinear Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Topographic Independent Component Analysis
Neural Computation
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Computer Vision and Image Understanding
Large-scale data exploration with the hierarchically growing hyperbolic SOM
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Component-based visual clustering using the self-organizing map
Neural Networks
Dynamic computational complexity and bit allocation for optimizing H.264/AVC video compression
Journal of Visual Communication and Image Representation
Self-organizing mixture models
Neurocomputing
Self-organization of probabilistic PCA models
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Projection pursuit mixture density estimation
IEEE Transactions on Signal Processing
Gaussian mixture models with covariances or precisions in shared multiple subspaces
IEEE Transactions on Audio, Speech, and Language Processing
On Transcoding a B-Frame to a P-Frame in the Compressed Domain
IEEE Transactions on Multimedia
Effective palette indexing for image compression using self-organization of Kohonen feature map
IEEE Transactions on Image Processing
Self Organizing Motor Maps for Color-Mapped Image Re-Indexing
IEEE Transactions on Image Processing
Optimally adaptive transform coding
IEEE Transactions on Image Processing
Power-rate-distortion analysis for wireless video communication under energy constraints
IEEE Transactions on Circuits and Systems for Video Technology
Image compression by self-organized Kohonen map
IEEE Transactions on Neural Networks
Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM)
IEEE Transactions on Neural Networks
Self-organizing mixture networks for probability density estimation
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
Links between PPCA and subspace methods for complete Gaussian density estimation
IEEE Transactions on Neural Networks
Restoration of images corrupted by Gaussian and uniform impulsive noise
Pattern Recognition
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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In this paper, we present a probabilistic neural model, which extends Kohonen's self-organizing map (SOM) by performing a probabilistic principal component analysis (PPCA) at each neuron. Several SOMs have been proposed in the literature to capture the local principal subspaces, but our approach offers a probabilistic model while it has a low complexity on the dimensionality of the input space. This allows to process very high-dimensional data to obtain reliable estimations of the probability densities which are based on the PPCA framework. Experimental results are presented, which show the map formation capabilities of the proposal with high-dimensional data, and its potential in image and video compression applications.