Learning Explicitly Conditioned Sparsifying Transforms

arXiv:2403.03168v1 Announce Type: cross
Abstract: Sparsifying transforms became in the last decades widely known tools for finding structured sparse representations of signals in certain transform domains. Despite the popularity of classical transforms such as DCT and Wavelet, learning optimal transforms that guarantee good representations of data into the sparse domain has been recently analyzed in a series of papers. Typically, the conditioning number and representation ability are complementary key features of learning square transforms that may not be explicitly controlled …

abstract arxiv cs.ai cs.lg cs.na data domain domains good math.na math.oc tools type wavelet