document: Generalized component analysis for text with heterogeneous attributes We present a class of richly structured , undirected hidden variable models suitable for simultaneously modeling text along with other attributes encoded in different modalities . Our model generalizes techniques such as principal component analysis to heterogeneous data types . In contrast to other approaches , this framework allows modalities such as words , authors and timestamps to be captured in their natural , probabilistic encodings . A latent space representation for a previously unseen document can be obtained through a fast matrix multiplication using our method . We demonstrate the effectiveness of our framework on the task of author prediction from 13 years of the NIPS conference proceedings and for a recipient prediction task using a 10-month academic email archive of a researcher . Our approach should be more broadly applicable to many real-world applications where one wishes to efficiently make predictions for a large number of potential outputs using dimensionality reduction in a well defined probabilistic framework .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O