… However, its potential for data compression has not yet been fully investigated in the … sparse
lowrank matrix approximation (SLRMA), an effective computational tool for data compression…

… We have described a compression scheme for low rank matrices. For a matrix A of dimensionality
m × n and rank k, the factorization can be applied to an arbitrary vector for the cost of (n …

… Although prohibitively large, such matrices are often approximately low rank. We propose …
a low rank decomposition of any matrix A as A ≈ LR, where L and R are the low rank factors. …

… compressed sensing; in Section 2, we introduce the fundamental theory of matrix rank
minimization and low-rank matrix … introduce the fundamental theory of low-rank matrix recovery; in …

… compression forms we focus on low-rank compression, whose roots lie in matrix algebra, and
where we replace a matrix W with another one having lower rank (… smaller matrices). That …

… method may have its own unique accuracy-compression ratio. … a low-rank DNN compression
algorithm that can solve the rank selection and freely control the degree of compression. …

… Existing compression methods are either lossy or introduce significant latency. We propose
… compression method that leverages low rank matrix factorization during training, to compress …

… matrix components usually reside in low-rank subspaces but some important entries are
sparsely scattered in the weight matrices and … for deep compression by the low-rank and sparse …

… the limits of compressing LSTM gates using low-rank matrix factorization and … Low-Rank
Matrix Factorization works better in general than pruning, except for particularly sparse matrices…

… sparse low rank (SLR) method which sparsifies SVD matrices to achieve better compression
rate by keeping lower rank … We demonstrate the effectiveness of our method in compressing …