Furthermore, dimensionality reduction techniques from Feature Extraction, Feature Selection from Machine Learning and an additional statistical approach are evaluated to be able to counteract the curse of dimensionality in textual processing. In doing so, the maintenance of data richness in communication ...
ATHENA is a Python package for reduction of high dimensional parameter spaces in the context of numerical analysis. It allows the use of several dimensionality reduction techniques such as Active Subspaces (AS), Kernel-based Active Subspaces (KAS), and Nonlinear Level-set Learning (NLL). It is ...
Principal component analysis (PCA) was performed using the Seurat function RunPCA() to identify the principal components that captured the most variation in the dataset. The top 15 principal components (PCs) were used for dimensionality reduction using the Seurat function FindNeighbors() and visualiza...
Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modelings process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, ...
Measuring radar cross-section (RCS) in high-noise environments remains a challenge. This paper presents an advanced signal processing framework that uses statistical dimensionality reduction to effectively separate the signal of interest from environmental noise. The proposed technique consists of two main...
Applying dimensionality reduction techniques, the system generates a list of suspect domains. Several models include intermediate stages: Attack Pyramid [6] is a model inspired by the attack tree concept proposed in [7] and [8]. Attack Pyramid uses the shape of a pyramid as a model of an ...
Advanced data exploration techniques like correlation analysis and dimensionality reduction help uncover hidden patterns and relationships in the data, providing valuable insights that can guide decision-making.CorrelationCorrelation is a statistical method used to evaluate the strength...
2 as the background where the work of researchers in detecting various cancers using learning techniques have been summarized and analyzed. The proposed methodology for detecting and classifying the type of cancer is shown in Sect. 3 followed by Sect. 4 where the model’s performance was ...
Quantum Principal Component Analysis (QPCA):QPCA is a quantum version of the classical Principal Component Analysis (PCA) algorithm. It utilizes quantum linear algebra techniques to extract the principal components from high-dimensional data, potentially enabling more efficient dimensionality reduction in ...
2. Unsupervised Feature Selection Techniques When there is no target variable available, unsupervised feature selection approaches can be used in order to reduce the dimensionality of the dataset while keeping its underlying structure. These methods often include changing the initial feature space into a...