Aug 2, 2023 · This method divides the raw image matrix into a low-rank denoised matrix, which retains all the information of images, and a sparse error matrix that captures ...

To address image classification challenges caused by noisy disturbances, a new algorithm called structurally incoherent adaptive weighted low-rank matrix ...

This paper presents a local subspace classifier (LSC) with Gabor filter decomposition for image classification. In our method, first, the training images are ...

To address image classification challenges caused by noisy disturbances, we propose a new algorithm called structurally incoherent adaptive weighted ...

Structurally incoherent adaptive weighted low-rank matrix decomposition for image classification · Computer Science, Engineering. Applied Intelligence · 2023.

Structurally incoherent adaptive weighted low-rank matrix decomposition for image classification. 2023, Applied Intelligence. Face Recognition via Compact ...

We used the low-rank feature to ensure that the given data are treated as two parts composed of the low-rank matrix and sparse matrix, representing useful ...

Missing: weighted | Show results with:weighted

We develop a notion of rank-sparsity incoherence, expressed as an uncertainty principle between the sparsity pattern of a matrix and its row and column spaces, ...

Apr 6, 2022 · RPCA decomposes a matrix into two matrices, L and S , by encouraging L to be low rank and S to be sparse as much as possible. It assumes that ...