Data sets and visualizations from Carrizosa, Guerrero, Romero Morales and Satorra (2018) 'Enhancing Interpretability in Factor Analysis by Means of Mathematical Optimization' (article/paper under submission) 
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'A natural approach to interpret the latent variables arising in Factor Analysis, called factors, consists of measuring explanatory variables over the same samples and assign (groups of) them to the factors. In this paper, we propose an optimization-based procedure to obtain the best assignment of the explanatory variables to a transformation of the factors, either including some information provided by the user based on his/her expertise or not. A new quality criterion to assess the interpretation to the factors given this way is also introduced. Our experimental results demonstrate the usefulness of our methodology' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Explanatory variables","Factor Analysis","Mixed Integer Nonlinear Programming"]} <|eot_id|>
 Here is the Description of a dataset. Description:'A natural approach to interpret the latent variables arising in Factor Analysis, called factors, consists of measuring explanatory variables over the same samples and assign (groups of) them to the factors. In this paper, we propose an optimization-based procedure to obtain the best assignment of the explanatory variables to a transformation of the factors, either including some information provided by the user based on his/her expertise or not. A new quality criterion to assess the interpretation to the factors given this way is also introduced. Our experimental results demonstrate the usefulness of our methodology' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
A natural approach to interpret the latent variables arising in Factor Analysis, called factors, consists of measuring explanatory variables over the same samples and assign (groups of) them to the factors. In this paper, we propose an optimization-based procedure to obtain the best assignment of the explanatory variables to a transformation of the factors, either including some information provided by the user based on his/her expertise or not. A new quality criterion to assess the interpretation to the factors given this way is also introduced. Our experimental results demonstrate the usefulness of our methodology
Explanatory variables; Factor Analysis; Mixed Integer Nonlinear Programming