Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with non-positive kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature Space Interpretation of SVMs with Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
Training SVM with indefinite kernels
Proceedings of the 25th international conference on Machine learning
Discriminatively regularized least-squares classification
Pattern Recognition
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning kernels from indefinite similarities
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
On discriminative semi-supervised classification
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Semi-supervised classification using local and global regularization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
Semi-Supervised Learning
Non-Euclidean or non-metric measures can be informative
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Indefinite kernel machines have attracted more and more interests in machine learning due to their better empirical classification performance than the common positive definite kernel machines in many applications. A key to implement effective kernel machine is how to use prior knowledge as sufficiently as possible to guide the appropriate construction of the kernels. However, most of existing indefinite kernel machines actually utilize the knowledge involved in data such as discriminative and structural information insufficiently and thus construct the indefinite kernels empirically. Discriminatively regularized least-squares classification (DRLSC) is a recently-proposed supervised classification method which provides a new discriminality-driven regularizer to encourage the discriminality of the classifier rather than the common smoothness. In this paper, we rigorously validate that the discriminative regularizer actually coincides with the definition on the inner product in Reproducing Kernel Kre@?n Space (RKKS) naturally. As a result, we further present a new discriminality-driven regularization framework for indefinite kernel machine based on the discriminative regularizer. According to the framework, we firstly reintroduce the original DRLSC from the viewpoint of the proper indefinite kernelization rather than the empirical kernel mapping. Then a novel semi-supervised algorithm is proposed in terms of different definition on the regularizer. The experiments on both toy and real-world datasets demonstrate the superiority of the two algorithms.