PCA (principal component analysis) can be effectively used for outlier detection by transforming data into a space where outliers are more easily identifiable due to the reduction in dimensionality and reshaping of data patterns.
This article demonstrates how basic statistics and techniques like PCA can be used to analyze tabular datasets, highlighting the importance of data preprocessing, statistical tests, and handling multicollinearity.
Exploratory data analysis (EDA) is a powerful technique to understand the structure of word embeddings, the basis of large language models. In this article, we'll apply EDA to GloVe word embeddings and find some interesting insights.
This article explains the PCA algorithm and its implementation in Python. It covers key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues. The tutorial aims to provide a solid understanding of the algorithm's inner workings and its application for dealing with high-dimensional data and the curse of dimensionality.
Discussion on the efficiency of Random Forest algorithms for PCA and Feature Importance. By Christopher Karg for Towards Data Science.
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