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Search · four archives
21 papers · ranked by Valyu relevance
Xiongjun Zhang, Michael K. Ng
Tensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi-dimensional nonnegative data, and has been an increasing interest in a variety of fields such as image processing, machine learning, and computer vision. In this paper, we propose a sparse nonnegative Tucker…
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…
Yuyuan Yu, Guoxu Zhou, Ning Zheng, Yuning Qiu + 2 more
'Qibin Zhao'] Abstract—Tensor ring (TR) decomposition is a powerful tool for exploiting the low-rank nature of multiway data and has demonstrated great potential in a variety of important applications. In this paper, nonnegative tensor ring (NTR) decomposition and graph regularized NTR (GNTR) decomposition are…
Arthur Marmin, José Henrique de Morais Goulart, Cédric Févotte
for nonnegative matrix factorization with the β-divergence and sparse regularization of one of the two factors (say, the activation matrix). It is well known that the norm of the other factor (th e dictionary matrix) needs to be controlled in order to avoid a n ill-posed formulation. Standard practice consists in…
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…
Qingshui Liao, Qilong Liu, Fatimah Abdul Razak
Tucker decomposition is widely used for image representation, data reconstruction, and machine learning tasks, but the calculation cost for updating the Tucker core is high. Bilevel form of triple decomposition (TriD) overcomes this issue by decomposing the Tucker core into three low-dimensional third-order factor…
Jeremy E. Cohen
Constrained tensor and matrix factorization models allow to extract interpretable patterns from multiway data. Therefore crafting efficient algorithms for constrained low-rank approximations is nowadays an important research topic. This work deals with columns of factor matrices of a low-rank approximation being sparse…
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…
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…
F. William Townes, Barbara E. Engelhardt
Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporate known…
Priyanka Shrestha, Luis Chumpitaz Diaz, Barbara E Engelhardt
Nonnegative spatial factorization (NSF) is a spatially-aware factorization method that uses Gaussian processes (GPs) as spatial priors in a Poisson latent factor model to robustly identify interpretable, parts-based representations in spatial transcriptomics data. However, NSF scales poorly with modern datasets due to…
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…
Charles Broadbent, Tianci Song, Rui Kuang
We used three methods based on the factorizations shown in [btae245-F1] as well as NSFH as baseline comparisons, focusing on linear methods that are able to extract spatial components in spatial transcriptomics data so that they can be used for direct quantitative and visual comparison to GraphTucker. 1. Nonnegative…
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…
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…
Ryota Kawasumi, Koujin Takeda
We study the problem of hyperparameter tuning in sparse matrix factorization under Bayesian framework. In the prior work, an analytical solution of sparse matrix factorization with Laplace prior was obtained by variational Bayes method under several approximations. Based on this solution, we propose a novel numerical…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Yanbo Lian, Anthony N. Burkitt
Sparse coding, predictive coding and divisive normalization have each been found to be principles that underlie the function of neural circuits in many parts of the brain, supported by substantial experimental evidence. However, the connections between these related principles are still poorly understood. In this…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…