Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Sparse classification for computer aided diagnosis using learned dictionaries
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Coarse-to-fine classification via parametric and nonparametric models for computer-aided diagnosis
Proceedings of the 20th ACM international conference on Information and knowledge management
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Multiclass classification is one of the core problems in many applications. High classification accuracy is fundamental to be accepted as a valuable or even indispensable tool in the work flow. In the classification problem, each sample is usually represented as a vector of features. Most of the cases, some features are usually redundant or misleading, and high dimension is not necessary. Therefore, it is important to find the intrinsically lower dimensional space to get the most representative features that contain the best information for classification. In this paper, we propose a novel dimension reduction method for multiclass classification. Using the constraint of the triplet set, our proposed method projects the original high dimensional feature space to a much lower dimensional feature space. This method enables faster computation, reduce the space needed, and mostly importantly produces more meaningful representations that leads to better classification accuracy.