A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement
Background As the world's largest coal producer, China was accounted for about 46% of
global coal production. Among present coal mining risks, methane gas (called gas in this …
global coal production. Among present coal mining risks, methane gas (called gas in this …
Fun with Flags: Robust Principal Directions via Flag Manifolds
Principal component analysis (PCA) along with its extensions to manifolds and outlier
contaminated data have been indispensable in computer vision and machine learning. In …
contaminated data have been indispensable in computer vision and machine learning. In …
Which objective weight method is better: PCA or entropy?
RMX Wu - Scientific Journal of Research & Reviews, 2022 - opus.lib.uts.edu.au
Multi-criteria decision-making (MCDM) methods have significantly been used for evaluating
and ranking critical factors with conflicting characteristics in different fields and disciplines …
and ranking critical factors with conflicting characteristics in different fields and disciplines …
Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization
This study proposed a robust artificial intelligence (AI) model based on the social behaviour
of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for …
of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for …
On the asymptotic L1-PC of elliptical distributions
The dominant eigenvector of the covariance matrix of a zero-mean data distribution
describes the line wherein the variance of the projected data is maximized. In practical …
describes the line wherein the variance of the projected data is maximized. In practical …
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
The use of orthogonal projections on high-dimensional input and target data in learning
frameworks is studied. First, we investigate the relations between two standard objectives in …
frameworks is studied. First, we investigate the relations between two standard objectives in …
Data dimensionality reduction technique for clustering problem of metabolomics data
In metabolomics studies, independent analyses or replicating the metabolite concentration
measurements are often performed to anticipate errors. On the other hand, the size of the …
measurements are often performed to anticipate errors. On the other hand, the size of the …
On the rotational invariant l1-norm PCA
Principal component analysis (PCA) is a powerful tool for dimensionality reduction.
Unfortunately, it is sensitive to outliers, so that various robust PCA variants were proposed in …
Unfortunately, it is sensitive to outliers, so that various robust PCA variants were proposed in …
A new formation of supervised dimensionality reduction method for moving vehicle classification
KS Chandrasekar, P Geetha - Neural Computing and Applications, 2021 - Springer
Analyzing a large number of features set for the classification process entails cost and
complexity. To reduce this burden, dimensionality reduction has been applied to the …
complexity. To reduce this burden, dimensionality reduction has been applied to the …
Robust PCA via Regularized Reaper with a Matrix-Free Proximal Algorithm
Principal component analysis (PCA) is known to be sensitive to outliers, so that various
robust PCA variants were proposed in the literature. A recent model, called reaper, aims to …
robust PCA variants were proposed in the literature. A recent model, called reaper, aims to …