Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis

Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD

This study explored various feature extraction methods for use in automated diagnosis of Attention-Deficit Hyperactivity Disorder (ADHD) from functional Magnetic Resonance Image (fMRI) data. Each participant's data consisted of a resting state fMRI scan as well as phenotypic data (age, gender, handedness, IQ, and site of scanning) from the ADHD-200 dataset. We used machine learning techniques t...

Feature reduction of hyperspectral images: Discriminant analysis and the first principal component

When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has...

Multiscale principal component analysis

Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between d...

Hyperspectral Imaging Target Detection Based on Improved Kernel Principal Component Analysis

The kernel principal component analysis (KPCA) algorithm has been extensively used in target detection and classification for hyperspectral imaging. Kernel parameters are a key component of KPCA, but no optimal method for selecting appropriate parameters has been proposed. We study the largest eigenvalue and sum of the eigenvalues of characteristic equation of kernel matrix, and then put forwar...

Learning from Data. Dimensionality Reduction: Principal Component Analysis