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Bayesian Distance Metric Learning on i-vector for Speaker Verification
i-vector score normalization
2015/3/9
Bayesian Distance Metric Learning on i-vector for Speaker Verification.
Bayesian Distance Metric Learning on i-vector for Speaker Verification
i-vector score normalization distance metric learning channel compensation limited training utterances
2014/11/27
This paper presents a new speaker verification system based on i-vector modeling as a feature extractor. In this modeling, we explore the distance constraints between i-vector pairs from the same spea...
Large-Margin Metric Learning for Partitioning Problems
Large-Margin Metric Learning Partitioning Problems
2013/4/28
In this paper, we consider unsupervised partitioning problems, such as clustering, image segmentation, video segmentation and other change-point detection problems. We focus on partitioning problems b...
Robustness and Generalization for Metric Learning
Metric learning Algorithmic robustness Generalization bounds
2012/11/23
Metric learning has attracted a lot of interest over the last decade, but little work has been done about the generalization ability of such methods. In this paper, we address this issue by proposing ...
Distance Metric Learning for Kernel Machines
metric learning distance learning support vector machines semi-de nite programming Mahalanobis distance
2012/9/17
Recent work in metric learning has signi cantly improved the state-of-the-art ink-nearest neighbor classi cation. Support vector machines (SVM), particularly with RBF kernels, are amongst the most pop...
Margin Emphasized Metric Learning and Its Application to Gabor Feature Based Face Recognition
face verification metric learning margin Gabor Labeled Faces in Wild
2013/7/24
In addressing side information based face recognition scenario, a new Margin Emphasized Metric Learning (MEML) method is proposed. As an improvement of previous metric learning, MEML defines a new obj...
SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity
Semi-supervised Metric Learning Paradigm Hyper Sparsity
2011/6/15
We consider the problem of learning a distance metric from a limited amount of pairwise information as effectively as possible. The proposed SERAPH (SEmi-supervised metRic leArning Paradigm with Hyper...
Efficient lp-Norm Multiple Feature Metric Learning for Image Categorization
Algorithms Performance Experimentation
2013/7/24
Previous metric learning approaches are only able to learn the metric based on single concatenated multivariate feature representation. However, for many real world problems with multiple feature repr...
Coupled Metric Learning for Face Recognition with Degraded Images
Coupled Metric Learning Face Recognition Degraded Images
2013/7/16
Real-world face recognition systems are sometimes confronted with degraded face images, e.g., low-resolution, blurred, and noisy ones. Traditional two-step methods have limited performance, due to the...
Coupled metric learning for face recognition with degraded images
Coupled metric learning face recognition
2010/12/20
Real-world face recognition systems are sometimes confronted with degraded face images, e.g., low-resolution, blurred, and noisy ones. Traditional two-step methods have limited performance, due to the...