Search · four archives
Search · four archives
16 papers · ranked by Valyu relevance
Christos Kolomvakis, Thomas Bobille, Arnaud Vandaele, Nicolas Gillis
Boolean matrix factorization (BMF) approximates a given binary input matrix as the product of two smaller binary factors. Unlike binary matrix factorization based on standard arithmetic, BMF employs the Boolean OR and AND operations for the matrix product, which improves interpretability and reduces the approximation…
Adolphus Wagala, Mehmet Samur, Giovanni Parmigiani
Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors. Boolean Matrix Factorization (BooMF) instead decomposes a binary matrix into two lower-rank binary matrices via logical AND and OR, expressing the data as a Boolean disjunction of interpretable…
Adolphus Wagala, Samur Mehmet, Giovanni Parmigiani
Boolean matrix factorization provides an interpretable framework for discovering latent binary patterns in high-dimensional data, yet existing methods typically analyze a single binary matrix or factorize multiple matrices independently, failing to exploit shared latent structure across related datasets. We propose…
Paul Haubenwallner, Matthias Heller
The conversion of functions to quantics tensor trains is a well-established procedure and can either be done analytically or numerically. Numerical conversion schemes are based on singular value decompositions, where access to the full tensor is necessary, or on cross interpolations, which only depend on sampling a…
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…
Mahbod Nouri, David Rotermund, Alberto Garcia-Ortiz, Klaus R. Pawelzik
Considering biological constraints in artificial neural networks has led to dramatic improvements in performance. Nevertheless, to date, the positivity of long-range signals in the cortex has not been shown to yield improvements. While Non-negative matrix factorization (NMF) captures biological constraints of positive…
Cindy Fang, Kelsey D. Montgomery, Sarah E. Maguire, Anthony D. Ramnauth + 8 more
Recent advances in spatially-resolved transcriptomics have enabled profiling of gene expression in a spatial context, which has led to the generation of large-scale single-cell and spatial atlases with computationally-derived cell type or spatial domain labels. An increasingly important task with these data has become…
Takeru Matsuda
The problem of predicting unobserved entries in a binary matrix, known as 1-bit matrix completion, has found diverse applications in fields such as recommendation systems. In this study, we develop an empirical Bayes method for 1-bit matrix completion motivated by the Efron--Morris estimator, a matrix generalization of…
Cristian Castiglione, Alexandre Segers, Lieven Clement, Davide Risso
Title: Summary Single-cell RNA sequencing allows the quantification of gene expression at the individual cell level, enabling the study of cellular heterogeneity and gene expression dynamics. Dimensionality reduction is a common preprocessing step critical for the visualization, clustering, and phenotypic…
Yu-Ting Liu, Timothy J Triche, Zachary J DeBruine
Large single-cell atlases now span tens of millions of cells, yet few provide reusable and interpretable reference representations that support direct biological reasoning at atlas-scale. Here, we present an interpretable Non-negative Matrix Factorization reference embedding of 28.5 million healthy cells and…
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…
Mohammad Abdollahi, Asiyeh Mirzaei Koli, Shokoofeh Ghiam, Changiz Eslahchi
Determining precise drug concentrations to inhibit cancer cell growth is a critical but resource-intensive challenge, especially for combinations requiring many dose pairs. Existing computational methods often predict synergy or classify interactions but rarely estimate exact concentration pairs for a defined…
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…
Peter Carbonetto, Abhishek Sarkar, Zihao Wang, Matthew Stephens
In an effort to develop topic modeling methods that can be quickly applied to large data sets, we revisit the problem of maximum-likelihood estimation in topic models. It is known, at least informally, that maximum-likelihood estimation in topic models is closely related to non-negative matrix factorization (NMF). Yet…
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…
András Telcs, Raúl Alcaraz
We develop a finite-resolution empirical framework for applying nonnegative Mages-Anastasiadi-Rohner partial information decomposition (MAR-PID) to continuous and non-binary discrete variables. The variables are represented by recursive quantile binarization. This provides a balanced binary-tree representation at each…