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Search · four archives
20 papers · ranked by Valyu relevance
Volkan Sevinç, Nikolas Kontemeniotis, Theodoros Perdikis, Michail Tsagris
Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negative data and has found widespread applications in fields such as bioinformatics, text mining, image analysis, and recommender systems. As the popularity of NMF has…
Michael R. Lindstrom, Xiaofu Ding, Feng Liu, Anand Somayajula + 1 more
'Deanna Needell'] Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In this paper, we show this factorization can be combined…
Renichiro Haba, Masayuki Ohzeki, Kazuyuki Tanaka, Dennis Salahub
Quantum annealing has garnered significant attention as meta-heuristics inspired by quantum physics for combinatorial optimization problems. Among its many applications, nonnegative/binary matrix factorization stands out for its complexity and relevance in unsupervised machine learning. The use of reverse annealing, a…
Xinyao Li, Akhilesh Tyagi, Loris Nanni
Over the last ten years, there has been a significant interest in employing nonnegative matrix factorization (NMF) to reduce dimensionality to enable a more efficient clustering analysis in machine learning. This technique has been applied in various image processing applications within the fields of computer vision…
Sajad Fathi Hafshejani, Devanshi Gaur, S. Hossain, R. Benkoczi
We propose a method for computing binary orthogonal nonnegative matrix factorization (BONMF) for clustering and classification. The method is tested on several representative real-world data sets. The numerical results confirm that the method has improved accuracy compared to the related techniques. The proposed method…
Denis Kleverov, Ekaterina Aladyeva, Alexey Serdyukov, Maxim N. Artyomov
Non-negative matrix factorization (NMF) is one of the most powerful linear algebra tools, which has found application in various areas of data analysis, including computational biology. Despite numerous optimization methods devised for NMF, our comprehension of the inherent topological structure within factorizable…
Ragnhild Laursen, Han Chen, Jack Demaray, Karin Pelka + 1 more
Methods for identifying complex multicellular spatial neighborhoods do not scale to existing spatial transcriptomics data, and often divide tissues into distinct neighborhoods with hard borders. We develop neighborhood NMF (NNMF) that identifies functionally coherent neighborhoods among heterogeneous cells. NNMF scales…
Rachid Hedjam, Abdelhamid Abdesselam, Abderrahmane Rahiche, Mohamed Cheriet
'Mohamed Cheriet'] The model described in this paper belongs to the family of non-negative matrix factorization methods designed for data representation and dimension reduction. In addition to preserving the data positivity property, it aims also to preserve the structure of data during matrix factorization. The idea…
Fogel, Paul, Geissler, Christophe + 2 more
This paper introduces the "Target Polish," a robust and computationally efficient framework for Non-Negative Matrix Factorization (NMF). Although conventional weighted NMF approaches are resistant to outliers, they converge slowly due to the use of multiplicative updates to minimize the objective criterion. In…
Mingming Li, Xingjie Wang, Chunhua Li, Anping Zeng + 1 more
Text embedding plays a crucial role in natural language processing (NLP). Among various approaches, nonnegative matrix factorization (NMF) is an effective method for this purpose. However, the standard NMF approach, fundamentally based on the bag-of-words model, fails to utilize the contextual information of documents…
Ragnhild Laursen, Han Chen, Jack Demaray, Karin Pelka + 1 more
Tissues consist of multi-cellular neighborhoods in which different cell types express correlated gene programs due to shared signaling environments. Methods for identifying these spatial neighborhoods may be powerful, but currently do not scale to existing data sets of millions of cells and often artificially divide…
Daniel Carbonero, Jad Noueihed, Christopher V. Gabel, Mark A. Kramer + 1 more
The widespread use of calcium imaging has produced large-scale datasets capturing neuronal population activity across diverse experimental contexts, posing challenges for analyzing complex, high-dimensional data. Dimensionality reduction (DR) methods have been pivotal in addressing these challenges by simplifying data…
Yukino Terui, Yuka Inoue, Yohei Hamakawa, Kosuke Tatsumura + 1 more
'Kazue Kudo'] Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix factorization techniques such as nonnegative matrix factorization (NMF) are often employed.…
Emine Güven
There is a great need to develop a computational approach to analyze and exploit the information contained in gene expression data. Recent utilization of non-negative matrix factorization (NMF) in computational biology has served its capability to derive essential details from a high amount of data in particular gene…
Eric Weine, Peter Carbonetto, Rafael A. Irizarry, Matthew Stephens
Poisson non-negative matrix factorization (NMF) is a widely used method to find interpretable "parts-based" decompositions of count data. While many variants of Poisson NMF exist, existing methods assume that the "parts" in the decomposition combine additively. This assumption may be natural in some settings, but not…
Ko Abe, Shintaro Yuki, Teppei Shimamura
Combinatorial indexing-based single-cell RNA sequencing methods such as sci-RNA-seq and sci-RNA-seq3 now enable the profiling of millions of cells, producing expression matrices that are both extremely sparse and high-dimensional. Conventional nonnegative matrix factorization (NMF) provides an interpretable framework…
Sajad Fathi Hafshejani, Zahra Moaberfard
Non-negative matrix factorization (NMF) has become a popular method for representing meaningful data by extracting a non-negative basis feature from an observed non-negative data matrix. Some of the unique features of this method in identifying hidden data put this method amongst the powerful methods in the machine…
Zuqi Li, Sam F. L. Windels, Noël Malod-Dognin, Seth M. Weinberg + 7 more
Combining omics and images, can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with…
Keisuke Ozawa
Statistically weighted principal component analysis (wPCA) is widely used to reduce the noise of scanning transmission electron microscopy-energy-dispersive X-ray (STEM-EDX) spectroscopy data. It is beneficial to retain the spatial resolution of observation in each step of the analysis, but the direct application of…
Diba Behnoudfar, Cory Simon, Joshua Schrier
Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas…