Parametric distance metric learning with label information

Authors:
Zhihua Zhang;James T. Kwok;Dit-Yan Yeung
Affiliations:
Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Venue:
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Year:
2003

Citing 2
Cited 14

Algorithmic Applications of Low-Distortion Geometric Embeddings

FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
A Nonlinear Feature Extraction Algorithm Using Distance Transformation

IEEE Transactions on Computers

Locally linear metric adaptation for semi-supervised clustering

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Comparison of relevance learning vector quantization with other metric adaptive classification methods

Neural Networks
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval

Pattern Recognition
Relaxational metric adaptation and its application to semi-supervised clustering and content-based image retrieval

Pattern Recognition
Classification of gene-expression data: The manifold-based metric learning way

Pattern Recognition
BoostCluster: boosting clustering by pairwise constraints

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Metric learning by discriminant neighborhood embedding

Pattern Recognition
Learning a Mahalanobis distance metric for data clustering and classification

Pattern Recognition
Distance metric learning and support vector machines for classification of mass spectrometry proteomics data

International Journal of Knowledge Engineering and Soft Data Paradigms
Retrieval based interactive cartoon synthesis via unsupervised bi-distance metric learning

MM '09 Proceedings of the 17th ACM international conference on Multimedia
Semi-supervised metric learning using pairwise constraints

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data

Pattern Recognition
Kernel-Based metric adaptation with pairwise constraints

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Local discriminative distance metrics ensemble learning

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Distance-based methods in pattern recognition and machine learning have to rely on a similarity or dissimilarity measure between patterns in the input space. For many applications, Euclidean distance in the input space is not a good choice and hence more complicated distance metrics have to be used. In this paper, we propose a parametric method for metric learning based on class label information. We first define a dissimilarity measure that can be proved to be metric. It has the favorable property that between-class dissimilarity is always larger than within-class dissimilarity. We then perform parametric learning to find a regression mapping from the input space to a feature space, such that the dissimilarity between patterns in the input space is approximated by the Euclidean distance between points in the feature space. Parametric learning is performed using the iterative majorization algorithm. Experimental results on real-world benchmark data sets show that this approach is promising.