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…
S. Basu, A. Banerjee, and R. Mooney. Proceedings of the 2004 SIAM International Conference on Data Mining, page 333--344. Lake Buena Vista, FL, Society for Industrial and Applied Mathematics, (April 2004)