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Search · four archives
22 papers · ranked by Valyu relevance
Daniel O’Malley, Velimir V. Vesselinov, Boian S. Alexandrov, Ludmil B. Alexandrov + 1 more
'Ludmil B. Alexandrov' 'Ivan Olier'] D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been…
Osman Asif Malik, Hayato Ushijima-Mwesigwa, Arnab Roy, Avradip Mandal + 2 more
Many fundamental problems in data mining can be reduced to one or more NP-hard combinatorial optimization problems. Recent advances in novel technologies such as quantum and quantum-inspired hardware promise a substantial speedup for solving these problems compared to when using general purpose computers but often…
Martin Slawski, Matthias Hein, Pavlo Lutsik
Motivated by an application in computational biology, we consider low-rank matrix factorization with {0, 1}-constraints on one of the factors and optionally convex constraints on the second one. In addition to the non-convexity shared with other matrix factorization schemes, our problem is further complicated by a…
Changlin Wan, Wennan Chang, Tong Zhao, Mengya Li + 2 more
'Chi Zhang'] Boolean matrix has been used to represent digital information in many fields, including bank transaction, crime records, natural language processing, protein-protein interaction, etc. Boolean matrix factorization (BMF) aims to decompose a boolean matrix via the product of two lowranked boolean matrices…
Ignacio Ramírez
—Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a particular case which has received significant attention for over thirty years, especially within the field of data mining. Dictionary learning…
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.…
Ameya Velingker, Maximilian Vötsch, David P. Woodruff, Samson Zhou
We introduce efficient (1 + ε)-approximation algorithms for the binary matrix factorization (BMF) problem, where the inputs are a matrix A ∈ {0, 1} n×d , a rank parameter k > 0, as well as an accuracy parameter ε > 0, and the goal is to approximate A as a product of low-rank factors U ∈ {0, 1} n×k and V ∈ {0, 1} k×d .…
Richard Kueng, Joel A. Tropp
This paper studies the problem of decomposing a low-rank positive-semidefinite matrix into symmetric factors with binary entries, either {±1} or {0,1}. This research answers fundamental questions about the existence and uniqueness of these decompositions. It also leads to tractable factorization algorithms that succeed…
Elijah Pelofske, Georg Hahn, Daniel O’Malley, Hristo N. Djidjev + 1 more
'Boian S. Alexandrov'] Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors…
Ellen Visscher, Michael A. Forbes, Christopher Yau
We present bfact, a Python package for performing accurate low-rank Boolean matrix factorisation (BMF). bfact uses a hybrid combinatorial optimisation approach based on a priori candidate factors generated from clustering algorithms. It selects the best disjoint factors before performing either a second combinatorial…
Amra Omanović, Hilal Kazan, Polona Oblak, Tomaž Curk
Background Matrix factorization methods are linear models, with limited capability to model complex relations. In our work, we use tropical semiring to introduce non-linearity into matrix factorization models. We propose a method called Sparse Tropical Matrix Factorization (STMF) for the estimation of missing (unknown)…
Miaoyan Wang, Lexin Li
We consider the problem of decomposing a higher-order tensor with binary entries. Such data problems arise frequently in applications such as neuroimaging, recommendation system, topic modeling, and sensor network localization. We propose a multilinear Bernoulli model, develop a rank-constrained likelihood-based…
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…
Sunho Park, Nabhonil Kar, Jae-Ho Cheong, Tae Hyun Hwang
Accurate identification of pathways associated with cancer phenotypes (e.g., cancer sub-types and treatment outcome) could lead to discovering reliable prognostic and/or predictive biomarkers for better patients stratification and treatment guidance. In our previous work, we have shown that non-negative matrix…
Aleksandar Poleksic
Past and current research in systems biology has taken for granted the Euclidean geometry of biological space. This has not only drawn parallels to other fields but has also been convenient due to the ample statistical and numerical optimization tools available to address the core task and downstream machine learning…
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…
He-Ming Chu, Jin-Xing Liu, Ke Zhang, Chun-Hou Zheng + 2 more
'Xiang-Zhen Kong'] Biclustering algorithm is an effective tool for processing gene expression datasets. There are two kinds of data matrices, binary data and non-binary data, which are processed by biclustering method. A binary matrix is usually converted from pre-processed gene expression data, which can effectively…
David Hirst, Morgane Térézol, Laura Cantini, Paul Villoutreix + 2 more
Joint matrix factorization is a popular method for extracting lower dimensional representations of multi-omics data. It disentangles underlying mixtures of biological signals, facilitating efficient sample clustering, disease subtyping, or biomarker identification, for instance. However, when a multi-omics dataset is…
Arni Sturluson, Ali Raza, Grant D. McConachie, Daniel Siderius + 2 more
Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A…
Kensuke Tanioka, Yuki Furotani, Satoru Hiwa
Low-rank approximation is used for interpreting the features of a correlation matrix using visualization tools; however, a low-rank approximation may result in estimation that is far from zero even if the corresponding original value is zero. In such a case, the results lead to a misinterpretation. To overcome this, we…
Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep + 3 more
Computer aided synthesis planning is a rapidly growing field for suggesting synthetic routes for molecules of interest. The methods used are usually dependent on access to large datasets for training, but with a finite experimental budget there are limitations on how much data can be obtained from experiments. Active…
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…