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Auto-Associative models and generalized Principal Component Analysis
Abstract : In this communication, we propose auto-associative (AA) models to generalize Principal component analysis (PCA). AA models have been introduced in data analysis from a geometrical point of view. They are based on the approximation of the observations scatter-plot by a differentiable manifold. They are interpreted as Projection pursuit models adapted to the auto-associative case. Their theoretical properties are established and are shown to extend the PCA ones. An iterative algorithm of construction is proposed and its principle is illustrated both on simulated and real data from image analysis.
Cited literature [6 references]
https://hal.inria.fr/hal-00985337
Contributor : Stephane Girard <>
Submitted on : Tuesday, April 29, 2014 - 3:16:10 PM
Last modification on : Wednesday, July 28, 2021 - 3:57:08 AM
Long-term archiving on: : Tuesday, July 29, 2014 - 12:45:10 PM
Contributor : Stephane Girard <>
Submitted on : Tuesday, April 29, 2014 - 3:16:10 PM
Last modification on : Wednesday, July 28, 2021 - 3:57:08 AM
Long-term archiving on: : Tuesday, July 29, 2014 - 12:45:10 PM
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