Results 21 to 30 of about 1,630,238 (295)
Boolean Matrix Factorization via Nonnegative Auxiliary Optimization
IEEE Access, 2021 A novel approach to Boolean matrix factorization (BMF) is presented. Instead of solving the BMF problem directly, this approach solves a nonnegative optimization problem with an additional constraint over an auxiliary matrix whose Boolean structure is ...
Duc P. Truong +3 more
doaj +1 more source
Linked Matrix Factorization [PDF]
Biometrics, 2018 AbstractSeveral recent methods address the dimension reduction and decomposition of linked high-content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set
Michael J. O'Connell, Eric F. Lock
openaire +3 more sources
Matrix Factorizations of the Discriminant of Sn
Journal of Symbolic Computation, 2023 Consider the symmetric group $S_n$ acting as a reflection group on the polynomial ring $k[x_1, \ldots, x_n]$, where $k$ is a field such that Char$(k)$ does not divide $n!$. We use Higher Specht polynomials to construct matrix factorizations of the discriminant of this group action: these matrix factorizations are indexed by partitions of $n$ and ...
Faber, E. +3 more
openaire +4 more sources
A Review on Quadrant Interlocking Factorization: WZ andWH Factorization
Journal of Nigerian Society of Physical Sciences, 2023 Quadrant Interlocking Factorization (QIF), an alternative to LU factorization, is suitable for factorizing invertible matrix A such that det(A) , 0.
Dlal Bashir +2 more
doaj +1 more source
Localization of Matrix Factorizations [PDF]
Foundations of Computational Mathematics, 2014 Matrices with off-diagonal decay appear in a variety of fields in mathematics and in numerous applications, such as signal processing, statistics, communications engineering, condensed matter physics, and quantum chemistry. Numerical algorithms dealing with such matrices often take advantage (implicitly or explicitly) of the empirical observation that ...
Ilya A. Krishtal +2 more
openaire +4 more sources
Zipf Matrix Factorization: Matrix Factorization with Matthew Effect Reduction [PDF]
2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), 2021 Recommender system recommends interesting items to users based on users' past information history. Researchers have been paying attention to improvement of algorithmic performance such as MAE and precision@K. Major techniques such as matrix factorization and learning to rank are optimized based on such evaluation metrics. However, the intrinsic Matthew
openaire +2 more sources
Lower Bounds for Matrix Factorization [PDF]
computational complexity, 2021 We study the problem of constructing explicit families of matrices which cannot be expressed as a product of a few sparse matrices. In addition to being a natural mathematical question on its own, this problem appears in various incarnations in computer science; the most significant being in the context of lower bounds for algebraic circuits which ...
Kumar, Mrinal, Volk, Ben Lee
openaire +5 more sources
Co-sparse Non-negative Matrix Factorization
Frontiers in Neuroscience, 2022 Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property.
Fan Wu +3 more
doaj +1 more source
BMC Bioinformatics, 2023 Background Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved ...
S. Morteza Hashemi +3 more
doaj +1 more source
IEEE Access, 2020 To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information.
Chenkun Zhang, Cheng Wang
doaj +1 more source