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Found 7 papers, 0 papers with code
A Safe Screening Rule with Bi-level Optimization of $ν$ Support Vector Machine
Support vector machine (SVM) has achieved many successes in machine learning, especially for a small sample problem.
Robust bilinear factor analysis based on the matrix-variate $t$ distribution
This introduces two challenges for $t$fa: (i) the inherent matrix structure of the data is broken, and (ii) robustness may be lost, as vectorized matrix data typically results in a high data dimension, which could easily lead to the breakdown of $t$fa.
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion
The Bayesian information criterion (BIC), defined as the observed data log likelihood minus a penalty term based on the sample size $N$, is a popular model selection criterion for factor analysis with complete data.
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data
To address the two problems, we propose RBLDA for MTS data classification, where each of the two within-class matrices is regularized via one parameter.
Robust factored principal component analysis for matrix-valued outlier accommodation and detection
To solve the robustness problem suffered by FPCA and make it applicable to matrix data, in this paper we propose a robust extension of FPCA (RFPCA), which is built upon a $t$-type distribution called matrix-variate $t$ distribution.
Highly tunable quadruple quantum dot in a narrow bandgap semiconductor InAs nanowire
It is shown that these current lines can be individually assigned as arising from resonant electron transport through the energy levels of different QDs.
Mesoscale and Nanoscale Physics
Spin selectivity through chiral polyalanine monolayers on semiconductors
Electrical generation of polarized spins in nonmagnetic materials is of great interest for the underlying physics and device potential.
Applied Physics Strongly Correlated Electrons
