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.
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.
Alternative to t-SNE and PCA
