Latent Factors
Latent factors are underlying, unobserved variables that explain the relationships between observed variables in a dataset. In data science and machine learning, latent factor analysis is a technique used to identify these underlying factors and use them to make predictions or recommendations. This technique is commonly used in collaborative filtering, a method for making personalized recommendations based on the preferences of similar users. Latent factor models can also be used for dimensionality reduction, where high-dimensional data is represented in a lower-dimensional space while preserving the most important information. This can be useful for visualizing complex data or reducing computational complexity in machine learning algorithms.
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