Phase transitions and sample complexity in Bayes-optimal matrix factorization
This work addresses a foundational problem in unsupervised representation learning and various applications like dictionary learning, but it is incremental as it builds on existing statistical mechanics tools.
The paper tackles the matrix factorization problem by analyzing the minimal mean-squared-error achievable in Bayes-optimal inference and the performance of an efficient approximate message passing algorithm, predicting promising results in terms of error and sample complexity.
We analyse the matrix factorization problem. Given a noisy measurement of a product of two matrices, the problem is to estimate back the original matrices. It arises in many applications such as dictionary learning, blind matrix calibration, sparse principal component analysis, blind source separation, low rank matrix completion, robust principal component analysis or factor analysis. It is also important in machine learning: unsupervised representation learning can often be studied through matrix factorization. We use the tools of statistical mechanics - the cavity and replica methods - to analyze the achievability and computational tractability of the inference problems in the setting of Bayes-optimal inference, which amounts to assuming that the two matrices have random independent elements generated from some known distribution, and this information is available to the inference algorithm. In this setting, we compute the minimal mean-squared-error achievable in principle in any computational time, and the error that can be achieved by an efficient approximate message passing algorithm. The computation is based on the asymptotic state-evolution analysis of the algorithm. The performance that our analysis predicts, both in terms of the achieved mean-squared-error, and in terms of sample complexity, is extremely promising and motivating for a further development of the algorithm.