Results 11 to 20 of about 24,084 (265)
Electronic Journal of Statistics, 2023 We investigate a general matrix factorization for deviance-based data losses, extending the ubiquitous singular value decomposition beyond squared error loss. While similar approaches have been explored before, our method leverages classical statistical methodology from generalized linear models (GLMs) and provides an efficient algorithm that is ...
Liang Wang, Luis Carvalho
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MatMat: Matrix Factorization by Matrix Fitting [PDF]
2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE), 2021 Matrix factorization is a widely adopted recommender system technique that fits scalar rating values by dot products of user feature vectors and item feature vectors. However, the formulation of matrix factorization as a scalar fitting problem is not friendly to side information incorporation or multi-task learning. In this paper, we replace the scalar
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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
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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
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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
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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
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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
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A survey on deep matrix factorizations [PDF]
Computer Science Review, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
De Handschutter, Pierre +2 more
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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
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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
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