principal component analysis, PCA

A popular decomposition called principal component analysis (PCA) is widely used for feature extraction.

Principal component analysis (PCA) and successive projections algorithm (SPA) were separately used to reduce variables of spectral model.

Generalization can be defined quantitatively and can be used to assess the performance of principal component analysis (PCA).

Subsequently, principal component analysis (PCA) is used for dimensionality reduction.

In the end of the paper, Principal Component Analysis (PCA) is used to search the Listed Automobile Company performance by the model.

Principal Component Analysis (PCA) is a main multivariate statistical method for getting principal information from observational data.

The Fuzzy Cluster and principal Component Analysis (PCA) fot classifying the brush--wood of the Shanhaiguan Forestry Farm in this paper.

Principal Component Analysis(PCA) method and nucleation theory are applied in configuration analysis produced by MD runs.

This paper proposes a new Fourier shape descriptor integrated with Principal Component Analysis(PCA) and the Farthest Point Distance(FPD).

Principal Component Analysis (PCA) and non-parametric tests were performed to assess relationships.

Secondly, a new weighted principal component analysis (PCA) method was designed to lower dimension for sample matrixes.

An efficient face recognition system is designed by using Discrete Wavelet Transform( DWT ), Principal Component Analysis(PCA) and WMMC.

This paper proposes a method based on Principal Component Analysis(PCA) combined with improved K-means for action potential classification.

Principal Component Analysis (PCA) is studied and some modifications are discussed to improve the performance of PCA.

The new neural network prediction model of water level is established based on the principal component analysis(PCA).

A new de-noising method for telemetry signal based on Principal Component Analysis (PCA) was proposed.

Radix salviae miltiorrhizae: by Principal Component Analysis(PCA), samples of near regions are near in scores plot;