Lost-in-Space Star Identification Using Planar Triangle Principal Component Analysis Algorithm

Modelling of some soil physical quality indicators using hybrid algorithm principal component analysis - artificial neural network

One of the important issues in the analysis of soils is to evaluate their features. In estimation of the hardly available properties, it seems the using of Data mining is appropriate. Therefore, the modelling of some soil quality indicators, using some of the early features of soil which have been proved by some researchers, have been considered. For this purpose, 140 disturbed and 140 undistur...

Fast Protein Superfamily Classification Using Principal Component Null Space Analysis

The protein family classification problem, which consists of determining the family memberships of given unknown protein sequences, is very important for a biologist for many practical reasons, such as drug discovery, prediction of molecular functions and medical diagnosis. Neural networks and bayesian methods have performed well on the protein classification problem, achieving accuracy ranging...

Vector ℓ0 latent-space principal component analysis

Principal component analysis (PCA) is a widely used signal processing technique. Instead of performing PCA in the data space, we consider the problem of sparse PCA in a potentially higher dimensional latent space. To do so, we zero-out groups of variables using vector `0 regularization. The estimation is based on the maximization of the penalized log-likelihood, for which we develop an efficien...

Fast principal component analysis using fixed-point algorithm

In this paper we present an efficient way of computing principal component analysis (PCA). The algorithm finds the desired number of leading eigenvectors with a computational cost that is much less than that from the eigenvalue decomposition (EVD) based PCA method. The mean squared error generated by the proposed method is very similar to the EVD based PCA method. 2007 Elsevier B.V. All rights ...

Feature Extraction Using Genetic Algorithm Based Principal Component Analysis