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Found 12 papers, 4 papers with code
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning
Nonnegative Matrix Factorization (NMF) is a widely applied technique in the fields of machine learning and data mining.
stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation
Spatially resolved transcriptomics represents a significant advancement in single-cell analysis by offering both gene expression data and their corresponding physical locations.
BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images
As a remedial measure, bone suppression techniques have been introduced.
Globally Convergent Accelerated Algorithms for Multilinear Sparse Logistic Regression with $\ell_0$-constraints
Multilinear logistic regression serves as a powerful tool for the analysis of multidimensional data.
An Accelerated Block Proximal Framework with Adaptive Momentum for Nonconvex and Nonsmooth Optimization
We propose an accelerated block proximal linear framework with adaptive momentum (ABPL$^+$) for nonconvex and nonsmooth optimization.
Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection
However, sPLS extracts the combinations between two data sets with all data samples so that it cannot detect latent subsets of samples.
Precise Facial Landmark Detection by Reference Heatmap Transformer
Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results.
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis
Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering.
Group-sparse SVD Models and Their Applications in Biological Data
We employ an alternating direction method of multipliers (ADMM) to solve the proximal operator.
Sparse Weighted Canonical Correlation Analysis
Given two data matrices $X$ and $Y$, sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors $u$ and $v$ to maximize the correlation between $Xu$ and $Yv$.
Network-regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
To address it, we introduce a novel network-regularized sparse LR model with a new penalty $\lambda \|\bm{w}\|_1 + \eta|\bm{w}|^T\bm{M}|\bm{w}|$ to consider the difference between the absolute values of the coefficients.
L0-norm Sparse Graph-regularized SVD for Biclustering
Motivated by the development of sparse coding and graph-regularized norm, we propose a novel sparse graph-regularized SVD as a powerful biclustering tool for analyzing high-dimensional data.
