Multimodal biometrics are always adopted to improve the recognition performance of single modality biometric systems. Besides introducing more discriminating power to the biometric system, integrating multiple modalities also leads to the curse of dimensionality problem. In this paper, we engage the minimal redundancy maximal relevance criterion to reduce the dimensionality of the feature vector. The minimal redundancy maximal relevance criterion is a feature selection criterion that aims to retain the most relevant elements while discarding the other redundant elements. Our experiments show that, with only 15% of the original feature length, minimal redundancy maximal relevance criterion-based features are able to perform similarly well or even better than the baseline results.
In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis. KDR method was extended and combined with the K-means clustering technique, Cox's proportional hazards regression model and log rank test where KDR contributes in gene classification to determine subgroups from the patient's group. Results from the experiments have indicated that our model has outperformed Support Vector Machines (SVM) in gene classification. We also observed that the best value for dimension of effective subspaces (K) for microarray DNA data is between 10%-20% of the total patients.
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