Local feature analysis: a statistical theory for information representation and transmission
PS Penev - 1998 - search.proquest.com
Low-dimensional representations of sensory signals are key to solving many of the
computational problems encountered in high-level vision. Principal Component Analysis …
computational problems encountered in high-level vision. Principal Component Analysis …
Local feature analysis: A general statistical theory for object representation
PS Penev, JJ Atick - Network: computation in neural systems, 1996 - Taylor & Francis
Low-dimensional representations of sensory signals are key to solving many of the
computational problems encountered in high-level vision. Principal component analysis …
computational problems encountered in high-level vision. Principal component analysis …
Topographic ica as a model of natural image statistics
A Hyvärinen, PO Hoyer, M Inki - International Workshop on Biologically …, 2000 - Springer
Independent component analysis (ICA), which is equivalent to linear sparse coding, has
been recently used as a model of natural image statistics and V1 receptive fields. Olshausen …
been recently used as a model of natural image statistics and V1 receptive fields. Olshausen …
[PDF][PDF] Dimensionality reduction by sparsification in a local-features representation of human faces
PS Penev - Unpublished, NEC Research Institute, Princeton, NJ, 1999 - Citeseer
Low-dimensional representations are key to solving problems in high-level vision, such as
face compression and recognition. On the basis of a multi-dimensional Gaussian model …
face compression and recognition. On the basis of a multi-dimensional Gaussian model …
Topographic independent component analysis as a model of V1 organization and receptive fields
A Hyvärinen, PO Hoyer - Neurocomputing, 2001 - Elsevier
Independent component analysis (ICA) has been recently used as a model of natural image
statistics and V1 simple cell receptive fields. Here we show how to extend the ICA model to …
statistics and V1 simple cell receptive fields. Here we show how to extend the ICA model to …
Learning visual spatial pooling by strong PCA dimension reduction
H Hosoya, A Hyvärinen - Neural computation, 2016 - direct.mit.edu
In visual modeling, invariance properties of visual cells are often explained by a pooling
mechanism, in which outputs of neurons with similar selectivities to some stimulus …
mechanism, in which outputs of neurons with similar selectivities to some stimulus …
Fast eigenspace decomposition of images of objects with variation in illumination and pose
RC Hoover, AA Maciejewski… - IEEE Transactions on …, 2010 - ieeexplore.ieee.org
Many appearance-based classification problems such as principal component analysis,
linear discriminant analysis, and locally preserving projections involve computing the …
linear discriminant analysis, and locally preserving projections involve computing the …
Topographic ICA as a model of V1 receptive fields
A Hyvarinen, P Hoyer, M Inki - Proceedings of the IEEE-INNS …, 2000 - ieeexplore.ieee.org
Independent component analysis (ICA), which is equivalent to linear sparse coding, has
been recently used as a model of natural image statistics and V1 receptive fields. Olshausen …
been recently used as a model of natural image statistics and V1 receptive fields. Olshausen …
Exploration of shape variation using localized components analysis
DA Alcantara, O Carmichael… - IEEE transactions on …, 2008 - ieeexplore.ieee.org
Localized components analysis (LoCA) is a new method for describing surface shape
variation in an ensemble of objects using a linear subspace of spatially localized shape …
variation in an ensemble of objects using a linear subspace of spatially localized shape …
Greedy kernel principal component analysis
This contribution discusses one aspect of statistical learning and generalization. The theory
of learning is very relevant to cognitive systems including cognitive vision. A technique …
of learning is very relevant to cognitive systems including cognitive vision. A technique …