We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions. Separability requires that the columns of the first NMF factor are equal to columns of the input matrix, while sparsity requires that the columns of the second NMF factor are sparse...
Learning the parts of objects by nonnegative matrix factorization 基于SCAD的稀疏非负矩阵分解的随机优化算法研究 非负矩阵 基于稀疏约束和流形正则的遥感图像非负矩阵解混 使用稀疏约束非负矩阵分解算法的跨年龄人脸识别 A penalized matrix decomposition with applications to sparse… 计算机专业外文翻译原文 翻译:非...
To achieve the best decomposition performance, some heuristic algorithms, such as particle swarm optimization [32] and grasshopper optimization [33] are used to optimize the parameters of VMD. Recently, Lee [34] used the sparse nonnegative matrix factorization to decompose the SCoh, different cyclos...
Non-negative matrix factorizationUnsupervised feature selectionSparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace ...
5.A Load-Balancing Algorithm for Sparse Matrix-Vector Multiplication on Parallel Computers并行计算稀疏矩阵乘以向量的负载平衡算法 6.New Scheme for Decomposition of Mixed Pixels Based on Constrained Nonnegative Matrix Factorization基于约束非负矩阵分解的混合象元分解新方法 ...
Non-negative matrix factorization (NMF) is a very efficient parameter-free method for decomposing multivariate data into strictly positive activations and basis vectors. However, the method is not suited for overcomplete representations, where usually sparse coding paradigms apply. We show how to merge...
Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative signals with the sparseness constraint. The signals are adequately represented by a set of basis vectors and the corresponding weight parameters. NMF has been successfully applied for blind source separation...
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 ...
Recently, sparsity-constrained non-negative matrix factorization (NMF) algorithms have been proved effective for hyperspectral unmixing (HU) since they can sufficiently utilize the sparsity property of HSIs. In order to improve the performance of NMF-based unmixing approaches, spectral and spatial ...
2.2 Sparse Nonnegative Matrix Factorization The nonnegative decomposition is in general not unique [9]. Furthermore, the features may not be parts-based if the data resides well inside the positive orthant. To address these issues, sparseness constraints have been imposed on the NMF problem. ...