Defect Severity-based Dimension Reduction Model using PCA

PCA - Based Dimension Reduction for Splines

PCA in a reproducing kernel Hilbert space is analysed as probabilistically optimal procedure of dimension reduction given a covariance structure by the reproducing kernel. It provides a unifying framework for various seemingly disparate and special techniques of dimension reduction applied to splines, in geostatistical “kriging” or in interpolation of data resulting from computer experiments. R...

Image Super Resolution Reconstruction Based MCA and PCA Dimension Reduction

Image super-resolution (SR) reconstruction is to reconstruct a high-resolution (HR) image from one or a series of low-resolution (LR) images in the same scene with a certain amount of prior knowledge. Learning based algorithm is an effective one in image super-resolution reconstruction algorithm. The core idea of the algorithm is to use the training examples of image to increase the high freque...

Model-based SIR for dimension reduction

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Learning Visual Spatial Pooling by Strong PCA Dimension Reduction

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 parameters are integrated so as to gain some extent of invariance to other parameters. For example, the classical energy model of phase-invariant V1 complex cells pools model simple cells preferring similar orientation but ...

Dimension Reduction for Power System Modeling Using Pca Methods Considering Incomplete Data Readings