Sentence examples for mean principal component from inspiring English sources

We calculate the Mahalanobis distance d Mahalanobis c from the mean principal component w mean c of each class: d Mahalanobis c = w i − w mean c T C − 1 w i − w mean c (10).

During recognition, we have to make a similar choice as in the previous approach to choose the number of eigenvectors that are used to calculate the Mahalanobis distance from the mean principal component vector of each class.

However, instead of finding the distance from the mean principal component of each class, we train PCA vectors using a support vector machine (SVM) with a radial basis function (RBF) kernel [25].

Our morphometric data comprised mean principal component scores from a previous study of cranial shape variation across all extant ursid species [2].

The authors mapped corsets with k-means, principal component analysis (PCA) and projective clustering algorithms deployed with massively parallel streaming data [69, 70].

Moreover, in order to discriminate among PAH sources and quantify their contributions, a source apportionment analysis of the collected data was provided by means Principal component Analysis (PCA) and Positive Matrix Factorization (PMF) methods.

The experimental results of this paper confirm one-class classifiers, including Auto-encoder Neural Network, k-means, principal component analysis data descriptor, minimum spanning tree data descriptor, k-nearest neighbour and Gaussian distribution as promising applications in creating an effective model for detecting buried archaeological sites.

This tool was developed for large-scale gene expression cluster analysis and integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines [ 12].

In the second phase the readily available MOX gas sensors were replaced by custom designed ones, which allowed single substances and classes of energetic materials by means to be discriminated by means of principal component analysis (PCA).

The mean weight vector (principal component) w mean c for each class is calculated using the first l weights that belong to the eigenvectors with the largest eigenvalues (l < k, where k is the total number of training sample images in all the classes combined, k c is the number of training samples in a class c, and l will be the dimension of w mean c ). w mean c = 1 k c ∑ u T. σ train c (8).

The selection of the most suitable columns was performed by means of principal component analysis.