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Search · four archives
19 papers · ranked by Valyu relevance
John Hood, Aaron Schein
Despite the ubiquity of multiway data across scientific domains, there are few userfriendly tools that fit tailored nonnegative tensor factorizations. Researchers may use gradient-based automatic differentiation (which often struggles in nonnegative settings), choose between a limited set of methods with mature…
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…
Nguyen, Manh, Pimentel-Alarcón, Daniel
Nonnegative matrix factorization (NMF) is a widely used tool for learning partsbased, low-dimensional representations of nonnegative data, with applications in vision, text, and bioinformatics [1, [2]]. In clustering applications, orthogonal NMF (ONMF) variants further impose (approximate) orthogonality on the…
Xiaoge Zhang, Zhengyu Fang, Kaiyu Tang, Huiyuan Chen + 1 more
Targeted drug therapies offer a promising approach for treating complex diseases, with combinational drug therapies often employed to enhance therapeutic efficacy. However, unintended drug-drug interactions may undermine treatment outcomes or cause adverse side effects. In this work, we propose a novel joint learning…
Jin Deng, Junjie Lan, Ruolan Du, Tao Xu + 4 more
The high recurrence rate of tumor limits the growth of precision medicine, whereas the exploration of correlations in multimodal data enables mining of features linked to tumor recurrence, ultimately identifying prospective biomarkers. Nevertheless, existing multimodal approaches centered on genetic molecular data…
Qiujing Lu, Tonmoy Monsoor, Ehsan Ebrahimzadeh, Kartik Sharma + 1 more
Nonnegative matrix factorization (NMF) seeks a low-rank approximation $X \approx UV^T$ with nonnegative factors and is commonly solved using interior methods that enforce feasibility throughout optimization. We show that such constraint-driven approaches can impede progress in the nonconvex landscape, leading to slow…
Zhaohang Zhang, Zhen Huang, Chunzhe Wang, Qiaowen Jiang + 1 more
Accurate mapping of localization error distribution is essential for assessing passive sensor systems and guiding sensor placement. However, conventional analytical methods like the Geometrical Dilution of Precision (GDOP) rely on idealized error models, failing to capture the complex, heterogeneous error distributions…
Ryan Swart, Johannes Brust
Symmetric nonnegative matrix factorization (Symmetric NMF) approximates a matrix as $WW^T$ with nonnegative rectangular factor $W$. It has broad applications in graph clustering and machine learning. In contrast to the NMF, projected gradient methods for the symmetric problem had been associated with slow convergence.…
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…
Avants, Brian B., Tustison, Nicholas J. + 2 more
Interpretable representation learning is a central challenge in modern machine learning, particularly in highdimensional settings such as neuroimaging, genomics, and text analysis. Current methods often struggle to balance the competing demands of interpretability and model flexibility, limiting their effectiveness in…
Ari Peden-Asarch, Meredith Weinstock, Kevin R. Coffey, John F. Neumaier
Miniscope calcium imaging provides a unique window into the activity of neurons during behavior while enabling spatial localization of individual cells across time. Despite its potential to revolutionize in vivo imaging alongside the rise of optogenetic tools, miniscopes remain underutilized. This gap may stem from the…
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…
Hongbin Lv, Meixiang Chen, Wen Li
The R-linear convergence of the NQZ algorithm for computing the H-spectral radius of a class of weakly irreducible nonnegative tensors is established by utilizing the directed graphs of tensors. Meanwhile, an upper bound for the root convergence factor R is derived and a general condition ensuring the linear…
Tingting Mu
Matrix factorisation is a fundamental tool for exploiting low-dimensional structure in high-dimensional data, with applications such as data compression, denoising, structure discovery, interpretable representation learning, and dimensionality reduction. Compared to conventional two-factor models, matrix…
Lara Kassab, Erin George, Deanna Needell, Haowen Geng + 2 more
There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical and approachable to the practitioner. Motivated by recent work on…
SeungJoo Lee, Yong-Chan Park, U. Kang, George Vousden
How can we accurately decompose a temporal irregular tensor along while incorporating a related knowledge graph tensor in both offline and online streaming settings? PARAFAC2 decomposition is widely applied to the analysis of irregular tensors consisting of matrices with varying row sizes. In both offline and online…
Chengyi Ma, Jiadong Mao, Kim-Anh Lê Cao
Integrating histological images with gene expression data offers a promising approach for linking tissue morphologies to molecular signatures and improving disease subtyping. However, such integration remains challenging due to the high dimensionality of these datasets, cross-modal heterogeneity, and limited…
Hassan Akell, Małgorzata Łazęcka, Dinesh Adhithya Haridoss, Miriam Urban + 2 more
Factor analysis is a dominant paradigm for multi-omic heterogeneous data, but is challenged by partially redundant signals and noise across views and by an unknown true number of factors. We present CLING, an unsupervised multi-view factor model with hierarchical Bayesian sparsity priors: a product-of-Gammas prior…
Authors not listed
The complete active space self-consistent field (CASSCF) method is essential for describing complex photochemical processes, but its application in ab initio molecular dynamics is often limited by the computational cost associated with four-center two-electron repulsion integrals (ERIs). We present the first…