Add to ORKGMetric learning is fundamental to lots of learning algorithms and it plays significant roles in many applications. In this paper, we present a LogDet divergence based met-ric learning approach to a learn Mahalanobis distance over input space of the instances. In the proposed model, the most natural con-straint triplets are used as the labels of the training samples. Meanwhile, in order to avoid overfitting problem, the model uses the LogDet divergence to regularize the obtained Mahalanobis matrix as close as possible to a given matrix. Besides, a cyclic iterative al-gorithm is presented to solve the objective function and accelerate the metric learning process. Furthermore, this paper constructs a novel dynamic triplets building strategy to...
Over the past decades, distance metric learning has attracted a lot of interest in machine learning ...
Distance metric learning plays an important role in many vision problems. Previous work of quadratic...
Recent studies [1]-[5] have suggested using constraints in the form of relative distance comparisons...
How to select and weigh features has always been a difficult problem in many image processing and pa...
Large data sets classification is widely used in many industrial applications. It is a challenging t...
For many machine learning algorithms such as k-nearest neighbor ( k-NN) classifiers and k-means clus...
A distance metric that can accurately reflect the intrinsic characteristics of data is critical for ...
For many machine learning algorithms such as k-nearest neighbor ( k-NN) classifiers and k-means clus...
A distance metric that can accurately reflect the intrinsic characteristics of data is critical for ...
Distance metric learning is of fundamental interest in machine learning because the employed distanc...
Mahalanobis Metric Learning (MML) has been actively studied recently in machine learning community. ...
The key to success of many machine learning and pattern recognition algorithms is the way of computi...
We propose a new method for local metric learning based on a conical combination of Mahalanobis metr...
Abstract—Distance metric learning is of fundamental interest in machine learning because the distanc...
Learning a distance metric provides solutions to many problems where the data exists in a high dimen...
Over the past decades, distance metric learning has attracted a lot of interest in machine learning ...
Distance metric learning plays an important role in many vision problems. Previous work of quadratic...
Recent studies [1]-[5] have suggested using constraints in the form of relative distance comparisons...
How to select and weigh features has always been a difficult problem in many image processing and pa...
Large data sets classification is widely used in many industrial applications. It is a challenging t...
For many machine learning algorithms such as k-nearest neighbor ( k-NN) classifiers and k-means clus...
A distance metric that can accurately reflect the intrinsic characteristics of data is critical for ...
For many machine learning algorithms such as k-nearest neighbor ( k-NN) classifiers and k-means clus...
A distance metric that can accurately reflect the intrinsic characteristics of data is critical for ...
Distance metric learning is of fundamental interest in machine learning because the employed distanc...
Mahalanobis Metric Learning (MML) has been actively studied recently in machine learning community. ...
The key to success of many machine learning and pattern recognition algorithms is the way of computi...
We propose a new method for local metric learning based on a conical combination of Mahalanobis metr...
Abstract—Distance metric learning is of fundamental interest in machine learning because the distanc...
Learning a distance metric provides solutions to many problems where the data exists in a high dimen...
Over the past decades, distance metric learning has attracted a lot of interest in machine learning ...
Distance metric learning plays an important role in many vision problems. Previous work of quadratic...
Recent studies [1]-[5] have suggested using constraints in the form of relative distance comparisons...