Deep non-negative matrix factorizationFunctional unitsTongue motionIntelligible speech is produced by creating varying internal local muscle groupings鈥攊.e., functional units鈥攖hat are generated in a systematic
A survey of deep nonnegative matrix factorization 2022, Neurocomputing Show abstract A review of multimodal image matching: Methods and applications 2021, Information Fusion Citation Excerpt : Most methods in this category tend to relax the constraints into an affordable form, thus giving rise to var...
nmfLS2: Non-negative Matrix Factorization with sparse matrix(Ji and Eisenstein, 2013)website Semi-NMF: Semi Non-negative Matrix Factorization Deep-Semi-NMF: Deep Semi Non-negative Matrix Factorization(Trigeorgis et al. 2014)website iNMF: Incremental Subspace Learning via NMF(Bucak and Gunsel, 20...
We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The proposed model specifically leverages pathway information to effectively reduce the dimensionality of omics data into a pathway a...
julia high-performance-computing differential-equations factorization nonlinear-equations sparse-matrix sparse-matrices newton-raphson steady-state bracketing equilibrium newton-method scientific-machine-learning sciml newton-krylov deep-equilibrium-models Updated Apr 7, 2025 Julia rico...
Li. Hyperspectral image super- resolution via non-local sparse tensor factorization. The IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), pages 5344–5353, 2017. [9] C. Dong, C. C. Loy, K. He, and X. Tang. Image super-resolution using deep convolutional networks. ...
[6] employs nonnegative matrix factorization (NMF) to delineate shared and dataset-specific features of cells across biosamples. Harmony [9] integrates scRNA-seq data by projecting cells into a shared embedding. Scanorama [13] leverages the matches of cells with similar transcriptional profiles ...
None of the above methods were applied to the MCA or other source separation problems and moreover it is non-trivial to obtain such extensions of these works. An unrolled nonnegative matrix factorization (NMF) algorithm (Roux et al. 2015) was implemented as a deep network for the task of...
whereM≈L,f(.) is a loss function used for the minimization term which depends on specific solvers or algorithms. Models like principal component analysis (PCA), non-negative matrix factorization (NMF), and matrix completion (MC) are in this category. ...
GRiNCH uses graph-regularized non-negative matrix factorization (NMF) to identify topologically associating domains (TADs) from a high-dimensional 3C count matrix (Fig. 1; see the “Methods” section). GRiNCH has several properties that make it attractive for analyzing these count matrices: (1) ...