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Nonnegative Unimodal Matrix Factorization
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021We introduce a new Nonnegative Matrix Factorization (NMF) model called Nonnegative Unimodal Matrix Factorization (NuMF), which adds on top of NMF the unimodal condition on the columns of the basis matrix. NuMF finds applications for example in analytical chemistry.
Andersen Man Shun Ang +3 more
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Nonnegative Matrix Factorization Based on Node Centrality for Community Detection
ACM Transactions on Knowledge Discovery from Data, 2022Community detection is an important topic in network analysis, and recently many community detection methods have been developed on top of the Nonnegative Matrix Factorization (NMF) technique.
Sixing Su +3 more
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Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey
arXiv.orgDimensionality Reduction plays a pivotal role in improving feature learning accuracy and reducing training time by eliminating redundant features, noise, and irrelevant data.
Farid Saberi-Movahed +4 more
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Label Propagated Nonnegative Matrix Factorization for Clustering
IEEE Transactions on Knowledge and Data Engineering, 2022Semi-supervised learning (SSL) that utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples is a powerful learning paradigm with widely real-world applications such as information retrieval and document ...
Long Lan +4 more
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IEEE Transactions on Neural Networks and Learning Systems, 2021
In this article, sparse nonnegative matrix factorization (SNMF) is formulated as a mixed-integer bicriteria optimization problem for minimizing matrix factorization errors and maximizing factorized matrix sparsity based on an exact binary representation ...
Hangjun Che, Jun Wang, A. Cichocki
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In this article, sparse nonnegative matrix factorization (SNMF) is formulated as a mixed-integer bicriteria optimization problem for minimizing matrix factorization errors and maximizing factorized matrix sparsity based on an exact binary representation ...
Hangjun Che, Jun Wang, A. Cichocki
semanticscholar +1 more source
Constrained Nonnegative Matrix Factorization Based on Label Propagation for Data Representation
IEEE Transactions on Artificial IntelligenceNonnegative matrix factorization (NMF) algorithms are a series of dimensional reduction techniques widely used in data preprocessing. To improve the performance of clustering and the discrimination of the low-dimensional representation in NMF, we ...
Junmin Liu +4 more
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Nonnegative Matrix Factorization
Proceedings of the 2015 ACM International Symposium on Symbolic and Algebraic Computation, 2015How quickly can we compute the nonnegative rank (r) of an m x n matrix? This problem ---- and the companion problem of finding a nonnegative matrix factorization with minimum inner-dimension ---- has a rich history, with applications in quantum mechanics, probability theory, data analysis, communication complexity and polyhedral combinatorics.
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Large-Cone Nonnegative Matrix Factorization
IEEE Transactions on Neural Networks and Learning Systems, 2016Nonnegative matrix factorization (NMF) has been greatly popularized by its parts-based interpretation and the effective multiplicative updating rule for searching local solutions. In this paper, we study the problem of how to obtain an attractive local solution for NMF, which not only fits the given training data well but also generalizes well on the ...
Tongliang, Liu +2 more
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IEEE Transactions on Geoscience and Remote Sensing
Unmixing is a crucial technique in analyzing hyperspectral imaging (HSI) data, which involves identifying the endmembers present in the data and estimating their abundance maps.
Tingting Wang +3 more
semanticscholar +1 more source
Unmixing is a crucial technique in analyzing hyperspectral imaging (HSI) data, which involves identifying the endmembers present in the data and estimating their abundance maps.
Tingting Wang +3 more
semanticscholar +1 more source
Labelwalking nonnegative matrix factorization
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion.
Long Lan +4 more
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