TL;DR
'kp-caIG' aims to provide a tool to select the most relevant variables based on the kernel PCA representation of the data as in Briscik et al. (2023).
This chapter presents tensor decomposition (TD)-based unsupervised feature extraction (FE) as a robust framework for feature selection in bioinformatics and provides a versatile and effective approach for extracting meaningful biological insights from complex, high-dimensional datasets.
Principal component analysis (PCA) is a widely used multivariate statistical technique for reducing the dimensionality of large datasets while retaining most of the original information. In this study, PCA was applied to identify the underlying socioeconomic components of household characteristics using data from 368 r...
In this paper we introduce the R package mccca, which implements multiple-class cluster correspondence analysis (MCCCA) proposed in M.Takagishi et al., (2022). MCCCA is a statistical method that identifies and visualizes heterogeneous tendencies specific to ``classes''(e.g., gender and nationality) in a low dimensional...
Envelope regression is a crucial part of multivariate linear modeling, leveraging separation into material and immaterial parts to provide parsimonious data reduction. Rekabdarkolaee et al. (2020) construct spatial envelope, a novel multivariate Gaussian process, extended by May et al. (2022) to linear coregionalizatio...
The CANDECOMP/PARAFAC (CP) decomposition is widely used for revealing the underlying patterns from multiway data (also referred to as a higher-order tensor). When the data is clipped or saturated, however, the linear CP model becomes unreliable. Several remedies exist such as treating the clipped entries as missing or...
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