Generalized Low Rank Models
This provides a unified framework for data analysis across diverse data types, which is incremental as it builds on existing techniques like PCA and matrix factorization.
The authors extended PCA to handle heterogeneous data types, enabling compression, denoising, and imputation across numerical, Boolean, categorical, and ordinal data simultaneously, and proposed parallel algorithms for fitting these models.
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, $k$-means, $k$-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features. We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results.