Self-weighted robust LDA for multiclass classification with edge classes
Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …
An overview and empirical comparison of distance metric learning methods
P Moutafis, M Leng, IA Kakadiaris - IEEE transactions on …, 2016 - ieeexplore.ieee.org
In this paper, we first offer an overview of advances in the field of distance metric learning.
Then, we empirically compare selected methods using a common experimental protocol …
Then, we empirically compare selected methods using a common experimental protocol …
Survey and experimental study on metric learning methods
D Li, Y Tian - Neural networks, 2018 - Elsevier
Distance metric learning has been a hot research spot recently due to its high effectiveness
and efficiency in improving the performance of distance related methods, such as k nearest …
and efficiency in improving the performance of distance related methods, such as k nearest …
Local discriminative distance metrics ensemble learning
The ultimate goal of distance metric learning is to incorporate abundant discriminative
information to keep all data samples in the same class close and those from different classes …
information to keep all data samples in the same class close and those from different classes …
Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions
Learning an appropriate distance metric is a critical problem in pattern recognition. This
paper addresses the problem of semi-supervised metric learning. We propose a new …
paper addresses the problem of semi-supervised metric learning. We propose a new …
Convex clustering with metric learning
The convex clustering formulation of Chi and Lange (2015) is revisited. While this
formulation can be precisely and efficiently solved, it uses the standard Euclidean metric to …
formulation can be precisely and efficiently solved, it uses the standard Euclidean metric to …
Dimension reduction by minimum error minimax probability machine
Dimension reduction is frequently adopted as a data preprocessing technique to facilitate
data visualization, interpretation, and classification. Traditional dimension reduction …
data visualization, interpretation, and classification. Traditional dimension reduction …
Regularized max-min linear discriminant analysis
G Shao, N Sang - Pattern recognition, 2017 - Elsevier
Several dimensionality reduction methods based on the max-min idea have been proposed
in recent years and can obtain good classification performance. In this paper, inspired by the …
in recent years and can obtain good classification performance. In this paper, inspired by the …
Heteroscedastic max-min distance analysis
Many discriminant analysis methods such as LDA and HLDA actually maximize the average
pairwise distances between classes, which often causes the class separation problem. Max …
pairwise distances between classes, which often causes the class separation problem. Max …
Single stage static level shifter design for subthreshold to I/O voltage conversion
YS Lin, DM Sylvester - Proceedings of the 2008 international symposium …, 2008 - dl.acm.org
A static subthreshold to I/O voltage level shifter is proposed. The proposed circuit employs a
diode-connected pull-up transistor stack and a feedback structure to alleviate the drive …
diode-connected pull-up transistor stack and a feedback structure to alleviate the drive …