Results 71 to 80 of about 138,910 (273)

Fairer non-negative matrix factorization

Frontiers in Big Data
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
Lara Kassab, Erin George, Deanna Needell, Haowen Geng, Nika Jafar Nia, Aoxi Li   +5 more
doaj   +1 more source

The Huang–Yang Formula for the Low‐Density Fermi Gas: Upper Bound

Communications on Pure and Applied Mathematics, EarlyView.
ABSTRACT We study the ground state energy of a gas of spin 1/2$1/2$ fermions with repulsive short‐range interactions. We derive an upper bound that agrees, at low density ϱ$\varrho$, with the Huang–Yang conjecture. The latter captures the first three terms in an asymptotic low‐density expansion, and in particular the Huang–Yang correction term of order
Emanuela L. Giacomelli, Christian Hainzl, Phan Thành Nam, Robert Seiringer   +3 more
wiley   +1 more source

Stretched non-negative matrix factorization

npj Computational Materials
A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis.
Ran Gu, Yevgeny Rakita, Ling Lan, Zach Thatcher, Gabrielle E. Kamm, Daniel O’Nolan, Brennan Mcbride, Allison Wustrow, James R. Neilson, Karena W. Chapman, Qiang Du, Simon J. L. Billinge   +11 more
doaj   +1 more source

Distance Matrix of a Class of Completely Positive Graphs: Determinant and Inverse

Special Matrices, 2020
A real symmetric matrix A is said to be completely positive if it can be written as BBt for some (not necessarily square) nonnegative matrix B. A simple graph G is called a completely positive graph if every matrix realization of G that is both ...
Das Joyentanuj, Jayaraman Sachindranath, Mohanty Sumit   +2 more
doaj   +1 more source

A Fast Gradient Method for Nonnegative Sparse Regression with Self Dictionary

, 2017
A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them.
Gillis, Nicolas, Luce, Robert
core   +1 more source

Quantifying Model Selection Uncertainty in Structural Analysis: Methodology and Application

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT With increasing focus on complex engineering systems under rare events, computational models are critical for predictions due to the scarcity or absence of data. However, selecting an appropriate model can be challenging. Using a single model without available test calibration could result in significant bias in performance predictions. A case
Ya‐Heng Yang, Tracy C. Becker
wiley   +1 more source

Support-based lower bounds for the positive semidefinite rank of a nonnegative matrix [PDF]

, 2013
The positive semidefinite rank of a nonnegative $(m\times n)$-matrix~$S$ is the minimum number~$q$ such that there exist positive semidefinite $(q\times q)$-matrices $A_1,\dots,A_m$, $B_1,\dots,B_n$ such that $S(k,\ell) = \mbox{tr}(A_k^* B_\ell)$.
Dirk, Oliver Theis, Troy Lee
core  

A Comparison of Realized Measures of Integrated Volatility: Price Duration‐ vs. Return‐Based Approaches

Journal of Forecasting, EarlyView.
ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann, Mawuli Segnon, Timo Wiedemann   +2 more
wiley   +1 more source

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, EarlyView.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 more
wiley   +1 more source

Stability Analysis of Discrete Hopfield Neural Networks with the Nonnegative Definite Monotone Increasing Weight Function Matrix

Discrete Dynamics in Nature and Society, 2009
The original Hopfield neural networks model is adapted so that the weights of the resulting network are time varying. In this paper, the Discrete Hopfield neural networks with weight function matrix (DHNNWFM) the weight changes with time, are considered,
Jun Li, Yongfeng Diao, Mingdong Li, Xing Yin   +3 more
doaj   +1 more source