Some Notes on Alternating Optimization
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Hierarchic Bayesian models for kernel learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Intrinsic dimensionality estimation of submanifolds in Rd
ICML '05 Proceedings of the 22nd international conference on Machine learning
Orthogonal Neighborhood Preserving Projections
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Manifold-adaptive dimension estimation
Proceedings of the 24th international conference on Machine learning
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
Combining feature spaces for classification
Pattern Recognition
IEEE Transactions on Image Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Feature Fusion Using Multiple Component Analysis
Neural Processing Letters
Evaluation of supervised and unsupervised 3D star visualisation algorithms
International Journal of Data Mining and Bioinformatics
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
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In most complex classification problems, many types of features have been captured or extracted. Feature fusion is used to combine features for better classification and to reduce data dimensionality. Kernelbased feature fusion methods are very effective for classification, but they do not reduce data dimensionality. In this brief, we propose an effective feature fusion method using locally linear embedding (LLE). The proposed method overcomes the limitations of LLE, which could not handle different types of features and is inefficient for classification. We propose an efficient algorithm to solve the optimization problem in obtaining weights of different features, and design an efficient method for LLE-based classification. In comparison to other kernel-based feature fusion methods, the proposed method fuses features to a significantly lower dimensional feature space with the same discriminant power. We have conducted experiments to demonstrate the effectiveness of the proposed feature fusion method.