Results 1 to 10 of about 1,436 (116)
Co-sparse Non-negative Matrix Factorization [PDF]
Frontiers in Neuroscience, 2022 Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property.
Fan Wu +3 more
doaj +3 more sources
Stretched non-negative matrix factorization
npj Computational MaterialsA novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis.
Ran Gu +11 more
doaj +3 more sources
Truncated Cauchy Non-Negative Matrix Factorization [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large errors, and develop a Truncated ...
Dacheng Tao +2 more
exaly +4 more sources
Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization
IEEE Access, 2018 Recommender Systems present a high-level of sparsity in their ratings matrices. The collaborative filtering sparse data makes it difficult to: 1) compare elements using memory-based solutions; 2) obtain precise models using model-based solutions; 3) get ...
Jesus Bobadilla +3 more
doaj +3 more sources
Non-negative Matrix Factorization for Dimensionality Reduction [PDF]
ITM Web of Conferences, 2022 —What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of ...
Olaya Jbari, Otman Chakkor
doaj +1 more source
Feature Weighted Non-Negative Matrix Factorization [PDF]
IEEE Transactions on Cybernetics, 2023 Non-negative Matrix Factorization (NMF) is one of the most popular techniques for data representation and clustering, and has been widely used in machine learning and data analysis. NMF concentrates the features of each sample into a vector, and approximates it by the linear combination of basis vectors, such that the low-dimensional representations ...
Mulin Chen, Maoguo Gong, Xuelong Li 0001
openaire +3 more sources
On Affine Non-Negative Matrix Factorization [PDF]
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007 We generalize the non-negative matrix factorization (NMF) generative model to incorporate an explicit offset. Multiplicative estimation algorithms are provided for the resulting sparse affine NMF model. We show that the affine model has improved uniqueness properties and leads to more accurate identification of mixing and sources.
Hans Laurberg, Lars Kai Hansen
openaire +1 more source
Discriminant projective non-negative matrix factorization. [PDF]
PLoS ONE, 2013 Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X.
Naiyang Guan +4 more
doaj +1 more source
Guided Semi-Supervised Non-Negative Matrix Factorization
Algorithms, 2022 Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform ...
Pengyu Li +6 more
doaj +1 more source
Non-negative Matrix Factorization on Manifold [PDF]
2008 Eighth IEEE International Conference on Data Mining, 2008 Recently non-negative matrix factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed
Deng Cai 0001 +3 more
openaire +1 more source