22 papers · ranked by Valyu relevance
Lucas A Gillenwater, Lawrence E Hunter, James C Costello
NMF factorizes a matrix of nonnegative values, $Y \inRm\timesn$, for $ m$ samples and $n$ features, into two matrices $W \inRm\timesk$ and $H \inRk\timesn$, where $k≪\text{min}{m,n}$. such that, $(2)Y∼WH$ The nonnegative constraints decompose the matrix into parts that are additive combinations. Each column in $Y$ is…
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…
Ling Zhong, Haiyan Gao, Friedhelm Schwenker
Clustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity. However, traditional NMF often struggles to effectively capture and preserve topological structure information between data during…
Zhen Wang, Wenwen Min
—Nonnegative Matrix Factorization (NMF) is a widely applied technique in the fields of machine learning and data mining. Graph Regularized Non-negative Matrix Factorization (GNMF) is an extension of NMF that incorporates graph regularization constraints. GNMF has demonstrated exceptional performance in clustering and…
Rachid Hedjam, Abdelhamid Abdesselam, Seyed Mohammad Jafar Jalali, Imran Khan + 1 more
'Imran Khan' 'Samir Brahim Belhaouari'] Various Non-negative Matrix factorization (NMF) based methods add new terms to the cost function to adapt the model to specific tasks, such as clustering, or to preserve some structural properties in the reduced space (e.g., local invariance). The added term is mainly weighted by…
Chong Peng, Yiqun Zhang, Yongyong Chen, Kang Zhao + 2 more
'Qiang Cheng'] Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation. However, current NMF methods do not always generate sparse…
Jingjing Liu, Nian Wu, Xianchao Xiu, Jianhua Zhang
Non-negative matrix factorization (NMF) is a popular unsupervised learning approach widely used in image clustering. However, in real-world clustering scenarios, most existing NMF methods are highly sensitive to noise corruption and are unable to effectively leverage limited supervised information. To overcome these…
Stuti Jain, Emilie Chouzenoux, Kriti Kumar, Angshul Majumdar
Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a…
Junhang Li, Jiao Wei, Can Tong, Tingting Shen + 5 more
'Shouliang Qi' 'Yudong Yao' 'Yueyang Teng'] Abstract—Traditional nonnegative matrix factorization (NMF) learns a new feature representation on the whole data space, whic h means treating all features equally. However, a subspace is often sufficient for accurate representation in practical applications, and redundant…
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…
Ziyu Liao, Tao Liu, Yue He, Longlong Lin + 1 more
Graph representation learning aims to map nodes or edges within a graph using low-dimensional vectors, while preserving as much topological information as possible. During past decades, numerous algorithms for graph representation learning have emerged. Among them, proximity matrix representation methods have been…
Jing Wu, Wenbo Li, Lijun Su, Huiru Wang + 2 more
In this paper, we present a modified nonmonotone line search algorithm that employs a variable parameter to control the degree of nonmonotonicity. This modification enhances both the probability of identifying the global minimum and the rate of convergence. Within the framework of alternating nonnegative least squares…
Shuyi Zhang, Jacob R. Leistico, Raymond J. Cho, Jeffrey B. Cheng + 1 more
Single-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge.…
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
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…
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…
Jeanette Johnson, Ashley Tsang, Jacob T. Mitchell, Emily Davis-Marcisak + 12 more
Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. Still, inferring biological processes requires additional post hoc statistics and annotation for interpretation of features learned from software packages developed for NMF implementation. Here, we aim to…
Jacqueline R. Thompson, Erik D. Nelson, Madhavi Tippani, Anthony D. Ramnauth + 17 more
The hippocampus contains many unique cell types, which serve the structure’s specialized functions, including learning, memory and cognition. These cells have distinct spatial organization, morphology, physiology, and connectivity, highlighting the importance of transcriptome-wide profiling strategies that retain…
Kasun Pathirage, Aman Virmani, Alison J. Scott, Richard J. Traub + 4 more
Mass spectrometry imaging (MSI) is a powerful scientific tool for understanding the spatial distribution of biochemical compounds in tissue structures. MSI data analysis presents problems due to the large file sizes and computational resource requirements and also due to the complexity of interpreting the raw spectral…
Ted Liefeld, Edwin Huang, Alexander T. Wenzel, Kenneth Yoshimoto + 4 more
Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…