In this article, I am going to show you how to choose the number of principal components when using principal component analysis for dimensionality reduction.
In the first section, I am going to give you a short answer for those of you who are in a hurry and want to get something working. Later, I am going to provide a more extended explanation for those of you who are interested in understanding PCA.
At the very beginning of the tutorial, I’ll explain the dimensionality of a dataset, what dimensionality reduction means, the main approaches to dimensionality reduction, the reasons for dimensionality reduction and what PCA means. Then, I will go deeper into the topic of PCA by implementing the PCA algorithm with the Scikit-learn machine learning library. This will help you to easily apply PCA to a real-world dataset and get results very fast.
Topic modelling refers to the task of identifying topics that best describes a set of documents. These topics will only emerge during the topic modelling process (therefore called latent). And one…
A. Putina, M. Bahri, F. Salutari, und M. Sozio. DSAA 2022 - IEEE International Conference on Data Science and Advanced Analytics, Paris / Virtual Event, France, (Oktober 2022)
K. Stelzner, K. Kersting, und A. Kosiorek. (2021)cite arxiv:2104.01148Comment: 15 pages, 3 figures. For project page with videos, see http://stelzner.github.io/obsurf/.