Fitted Latent Factors Model

A Fitted Latent Factors Model is a fitted model that is based on a latent factors model family.

• Context:
  • It can be created by a Latent Factors Model Training System (that solves a latent factors model training task).
  • …
• Counter-Example(s):
  • a Fitted Regression Model.
• See: Latent Factor, PCA Model, SVD Model.

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References

2017

• (Sammut & Webb, 2017) ⇒ (2017). "Latent Factor Models and Matrix Factorization". In: (Sammut & Webb, 2017)
  • QUOTE: Latent Factor models are a state of the art methodology for model-based collaborative filtering. The basic assumption is that there exist an unknown low-dimensional representation of users and items where user-item affinity can be modeled accurately. For example, the rating that a user gives to a movie might be assumed to depend on few implicit factors such as the user’s taste across various movie genres. Matrix factorization techniques are a class of widely successful Latent Factor models that attempt to find weighted low-rank approximations to the user-item matrix, where weights are used to hold out missing entries. There is a large family of matrix factorization models based on choice of loss function to measure approximation quality, regularization terms to avoid overfitting, and other domain-dependent formulations.

2010

• (Hagan & West, 2010) ⇒ Anthony O' Hagan, and ‎Mike West, editors. (2010). “The Oxford Handbook of Applied Bayesian Analysis. ISBN:0191613894
  • … Section 6.2.4 describes exploratory analysis of the fitted latent factor model, highlighting associations between factors and several clinical variables. Section …

    … at termination, the latent factor model included 213 genes that were among the original 8,509 and an additional 287 not appearing in the in vitro analysis.

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