Latent Factor Analysis
Latent Factor Analysis is a statistical method used to identify underlying factors that explain the patterns of correlations within a set of observed variables. In the context of Data Science, it is commonly used for dimensionality reduction and feature extraction. It is often applied in recommendation systems, where it can be used to identify latent factors that explain user preferences and make personalized recommendations. Latent Factor Analysis can also be used for data visualization, clustering, and classification. The method assumes that the observed variables are linearly related to a smaller number of unobserved variables, called latent factors, which capture the underlying structure of the data. The goal of Latent Factor Analysis is to estimate the values of the latent factors and their relationships with the observed variables, based on the observed data. This is typically done using techniques such as Principal Component Analysis (PCA) or Factor Analysis (FA).
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