[PDF] Top 20 On rationality of nonnegative matrix factorization
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On rationality of nonnegative matrix factorization
... the nonnegative ranks over R and Q coincide for matrices of rank at most ...of nonnegative rank (over R ) at most ...the nonnegative rank of a matrix can indeed depend on the underlying field: ... See full document
17
Hierarchical community detection via rank-2 symmetric nonnegative matrix factorization
... adjacency matrix; and ‘x’ indicates that an approxi- mated normalized cut is computed using the normalized adjacency matrix, which usually results in faster ...adjacency matrix, which does not have a ... See full document
26
A Robust Symmetric Nonnegative Matrix Factorization Framework for Clustering Multiple Heterogeneous Microbiome Data
... Fig.5 illustrates the clear clustering patterns on HMP data. As RSNMF and other variants of NMF are soft-clustering based methods, the coefficient matrix H (637×7) which is a low-dimension representation of ... See full document
14
Bayesian group sparse learning for music source separation
... [1]. Nonnegative matrix factorization (NMF) is designed to find an approximate factorization X ≈ AS for a data matrix X into a basis matrix A and a weight matrix S which ... See full document
15
Identification of Nine lncRNAs Signature for Predicting Survival Benefit of Melanoma Patients Treated with Immune Checkpoint Inhibitors
... then nonnegative matrix factorization (NMF) was conducted to cluster patients into two different cluster and compared them regarding overall survival (OS) and progression-free survival ... See full document
17
Determining Patterns in Neural Activity for Reaching Movements Using Nonnegative Matrix Factorization
... of nonnegative bases which spans the data in the positive ...bin-count matrix, each row contains many zero en- tries (zero bin counts) even after removing nonfiring neu- rons since most neurons do not fire ... See full document
9
Robust Multi Relational Clustering via ℓ1 Norm Symmetric Nonnegative Matrix Factorization
... Traditional clustering aims to partition data points into several groups, such that the data points in the same group can share some commonalities whilst those from different groups are dissimilar. With the recent ... See full document
5
A Projected Alternating Least square Approach for Computation of Nonnegative Matrix Factorization
... Recently, the main proposed NMF methods are based on the alternating nonnegative least squares (ANLS) framework. Methods like projected Quasi-Newton [8], projected gradient [9], active set [10], [11] and block ... See full document
8
Exploiting Nonnegative Matrix Factorization with Mixed Group Sparsity Constraint to Separate Speech Signal from Single-channel Mixture with Unknown Ambient Noise
... normal matrix multiplication, B ∈ R F + × K is the spectral basis matrix whose column vectors are spectral characteristics appearing in V, A ∈ R K + × M is the activation matrix whose row vectors are ... See full document
8
Note Onset Detection via Nonnegative Factorization of Magnitude Spectrum
... The factorization rank R is set to 3, that is, exactly the same as the total number of the notes in the ...generated nonnegative magnitude spectrum matrix X is visualized in Figure ... See full document
15
Double regularized matrix factorization for image classification and clustering
... Regularized Nonnegative Self-Representation (GRNSR) [19] and Structure Preserving Nonnegative Feature Self-Representation (SPNFSR) ...weight matrix can be obtained automatically via the learned ... See full document
19
Two-hierarchical nonnegative matrix factorization distinguishing the fluorescent targets from autofluorescence for fluorescence imaging
... initialization matrix [12] to simulate the quintessential fluorochrome spectra, while other works [15] exploit the statistical characteristics of the source spec- tra signals for the initialization of S ... See full document
19
A new joint CTC-attention-based speech recognition model with multi-level multi-head attention
... A method called joint connectionist temporal classification (CTC)-attention-based speech recognition has recently received increasing focus and has achieved impressive performance. A hybrid end-to-end architecture that ... See full document
12
Innovative Algorithms and Evaluation Methods for Biological Motif Finding
... We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization SNMF method is used with Chou-F[r] ... See full document
222
Improving knowledge on the activation of bone marrow fibroblasts in MGUS and MM disease through the automatic extraction of genes via a nonnegative matrix factorization approach on gene expression profiles
... via nonnegative matrix factorization The gene expression profile obtained by the biologi- cal experiments are collected in numerical nonnegative matrices whose columns measure the processed ... See full document
16
Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
... problem can be solved in polynomial time, even if some noise is added to the separable matrix M (Arora et al., 2012a). Algebraically, separability means that there exists a rank-r NMF (W, H) ≥ 0 of M where each ... See full document
32
A study of blind source separation using nonnegative matrix factorization
... During the past decades, there are several methods or techniques have been proposed to improve the blind source separation (BSS) in signal processing field. One of the methods has been declared as useful technique which ... See full document
7
Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing
... original matrix and NMF applied on the preprocessed matrix is not very informative in itself: while the former will feature a lower approx- imation error, the latter will provide a sparser part-based ... See full document
38
Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability
... We present a numerical algorithm for nonnegative matrix factorization (NMF) problems under noisy separability. An NMF problem under separability can be stated as one of finding all vertices of the ... See full document
29
Various Techniques for Predicting Cervical Cancer
... penalized matrix decomposition (PMD), nonnegative matrix factorization (NMF), meta sample based SR classification (MSRC), tumor classification based on correlation filters and gene ... See full document
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