Simple principal component analysis using Lasso

demonstrating buried channels using principal component analysis

spectral decomposition of time series has a significant role in seismic data processing and interpretation. since the earth acts as a low-pass filter, it changes frequency content of passing seismic waves. conventional representing methods of signals in time domain and frequency domain cannot show time and frequency information simultaneously. time-frequency transforms upgraded spectral decompo...

Multiresolution using principal component analysis

This paper proposes Principal Component Analysis (PCA) to find adaptive bases for multiresolution. An input image is decomposed into components (compressed images) which are uncorrelated and have maximum l2 energy. With only minor modification, a single layer linear network using the Generalized Hebbian Algorithm (GHA) is used for multiresolution PCA. The decomposition has been successfully app...

Principal Component Analysis using R

This tutorial is designed to give the reader a short overview of Principal Component Analysis (PCA) using R. PCA is a useful statistical method that has found application in a variety of fields and is a common technique for finding patterns in data of high dimension. Consider we are confronted with the following situation: The data, we want to work with, are in form of a matrix (xij)i=1...N,j=1...

Principal Component Projection Without Principal Component Analysis

We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. By avoiding explicit principal component analysis (PCA), our algorithm is the first with no runtime dependen...

A modified principal component technique based on the LASSO