Latent Factor Model (LFM) is a category of unsupervised methods that attempt to model observed data examples by a linear combination (coefficients) of latent unobserved variables (factors). Motivated by a desire to capture commonalities among multiple observed variables, a latent factor model has been used to explore the hidden information of the data in various areas such as psychology, bioinformatics or signal processing.
Classical latent factor models are formulated as either maximizing the variance of the factors (i.e., Principal Component Analysis (PCA)) or minimizing the error of reconstructing data by the latent factors (i.e., Matrix Factorization). The base vectors are therefore orthogonal and corresponding their coefficients are uncorrelated. Due to the simple form and computation convenience, the latent factor model has been used in modeling and analyzing data sets such as text documents and images. However, in many cases, orthogonality assumptions are so strong that it is difficult to explain the hidden factors.
Consequently, methods, structures or techniques that address such aspects would represent a significant advance in the art.