Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Robust De-noising by Kernel PCA
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Incorporating Prior Knowledge into SVM for Image Retrieval
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
Automatic dimensionality selection from the scree plot via the use of profile likelihood
Computational Statistics & Data Analysis
Soft SVM and Its Application in Video-Object Extraction
IEEE Transactions on Signal Processing - Part I
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Posterior probability support vector Machines for unbalanced data
IEEE Transactions on Neural Networks
Robust kernel discriminant analysis using fuzzy memberships
Pattern Recognition
RF-PCA2: an improvement on robust fuzzy PCA
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Information Sciences: an International Journal
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Support vector machine (SVM) is a theoretically ell motivated algorithm developed from statistical learning heory, that have shown good performance in many fields. In pite of its success, it still suffers from a noise sensitivity problem. To relax this problem, the SVM was extended by the introduction of fuzzy memberships to the fuzzy SVM (FSVM). The FSVM also has been extended further in two ways: by adopting a different objective function with the help of domain-specific knowledge and by employing a different membership calculation method. In this paper, we propose a new membership calculation method, that belongs to the second group. It is different from previous ones in that it does not assume any simple data distribution and does not need any prior knowledge. The proposed method is based on reconstruction error, which measures the agreement between the overall data structure and a data point. Thus the reconstruction error can represent the degree of outlier-ness and help in achieving noise robustness. Experimental results with synthetic and real data sets also support this.