We are pleased to announce that our work “Efficient Identification of Butterfly Sparse Matrix Factorizations” (Léon Zheng, Elisa Riccietti, Rémi Gribonval) has been accepted for publication in SIAM Journal on Mathematics of Data Science.
This work studies identifiability aspects of sparse matrix factorizations with butterfly constraints, a structure associated with fast transforms and used in recent neural network compression methods for its expressiveness and complexity reduction properties. In particular, we show that the butterfly factorization algorithm from the article “Fast learning of fast transforms, with guarantees” (ICASSP 2022) is endowed with exact recovery guarantees.
