Dimensionality Reduction in Machine Learning: PCA and t-SNE.
Dimensionality reduction is a key technique in machine learning for simplifying datasets by reducing the number of features while retaining important information. Principal Component Analysis (PCA) is a linear method that transforms data into principal components ordered by variance, while t-SNE is a non-linear method ideal for visualizing high-dimensional data in lower dimensions. Both methods are widely used in data science for tasks like visualization and feature extraction.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3708665) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Kelvin Posted on May 1 Dimensionality Reduction in Machine Learning: PCA and t-SNE. #machinelearning #datascience #algorithms #ai Dimensionality reduction is a fundamental concept in machine learning used to reduce the number of input features (dimensions) in a dataset while preserving as much important information as possible.
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