Tag: unsupervised-learning

• Why the simplest explanation isn’t always the best

  Eva L. Dyer and Konrad Kording discuss in a commentary article “Why the simplest explanation isn’t always the best” an essential learning related to the article Phantom oscillations in principal component analysis (also available on BioRXiv) Dimensionality reduction simplifies high-dimensional data into a small number of representative patterns. One dimensionality reduction method, principal component analysis (PCA), often selects oscillatory…

  walterstiers

  2024-04-18

  Biology of Information, Complexity, Information Technology, Neurobiology/psychology, Science

  #DecisionIntelligence, #TheInformationLens, AI, artificial-intelligence, data-science, machine-learning, unsupervised-learning