Computation of training set (X^T * W * X) and (X^T * W * Y) or (X^T * X) and (X^T * Y) in a cross-validation setting using the fast algorithms by Engstrøm and Jensen (2025). FELBuilder is an automated ...

Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...

Department of Psychology, SVKM’s Mithibai College of Arts, Chauhan Institute of Science and Amrutben Jivanlal College of Commerce and Economics (Empowered Autonomous), Mumbai, India Introduction: ...

Background/objectives: Dietary patterns play an important role in regulating serum uric acid (SUA) levels in the body. Recently, compositional data analysis (CoDA) has been proposed as an alternative ...

Amid the wave of the digital age, advanced technologies such as big data, artificial intelligence, and cloud computing are driving precise analysis and forecasting across various fields. This paper ...

Abstract: cross-dimensional principal component analysis (CD-PCA). It is based on the semi-tensor product of matrices theory (STP), where a new projection rule is introduced to reduce dimensionality ...

With the increasing complexity of analytical data nowadays, great reliance on statistical and chemometric software is quite common for scientists. Powerful open-source software, such as Python, R, and ...

ABSTRACT: This study applies Principal Component Analysis (PCA) to evaluate and understand academic performance among final-year Civil Engineering students at Mbeya University of Science and ...

Abstract: Robust tensor principal component analysis (RTPCA) based on tensor singular value decomposition (t-SVD) separates the low-rank component and the sparse component from the multiway data. For ...

Ensure your data are in the proper format. The first column (index 0) should consist of the species name or other identifying information. The second column (index 1) should consist of a standard body ...