Blogs on curse of dimensionality

00 Blog Studis fuer Studis LDA - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Linear discriminant analysis: A method for dimension reduction of classifiable data sets

19. April 2023

Large amounts of data are problematic when calculating ML algorithms. One solution lies in dimension reduction methods such as linear discriminant analysis (LDA), which reduces the number of data features while taking into account the existing class labels....

00 Blog Studis fuer Studis PCA - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Basics

Principal component analysis – A method for dimension reduction on large amounts of data

12. April 2023

Learning on large, high-dimensional data sets provides particularly meaningful results, but consumes a lot of memory and computing capacity. The dimension reduction method can help to solve this problem....

00 Blog Weichert Forschung neu - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Basics

Using Design of Experiments and Artificial Intelligence to achieve greater sustainability through resource efficiency

30. November 2022

Artificial Intelligence can promote sustainability in research and development. With its targeted use in Design of Experiments, it is possible to save on experiments and therefore also resources....

00 Blog Weichert DoE - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Basics

How does Design of Experiments work with AI?

23. March 2022

In research and development departments, Design of Experiments with Artificial Intelligence can save many experiments and therefore time, money and resources. But how exactly does it work?...

00 Blog Weichert Daten - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Basics

How much data does Artificial Intelligence need?

19. May 2021

Artificial Intelligence needs a lot of data. But how much data do I need as a minimum for a good model? The "Curse of Dimensionality" and the "Nyquist-Shannon Theorem" provide answers....