Denise: Deep Learning based Robust PCA for Positive Semidefinite Matrices

Calypso Herrera, Florian Krach, Josef Teichmann

We introduce Denise, a deep learning based algorithm for decomposing positive semidefinite matrices into the sum of a low rank plus a sparse matrix. The deep neural network is trained on a randomly generated dataset using the Cholesky factorization. This method, benchmarked on synthetic datasets as well as on some S&P500 stock returns covariance matrices, achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computational time. Finally, theoretical results concerning the convergence of the training are derived.

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