Ensemble learning based on multiple kernel learning for hyperspectral chemical plume detection

Prudhvi Gurram; Heesung Kwon

doi:10.1117/12.850101

12 May 2010 Ensemble learning based on multiple kernel learning for hyperspectral chemical plume detection

Prudhvi Gurram, Heesung Kwon

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 76951U (2010) https://doi.org/10.1117/12.850101
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Recently, a SVM-based ensemble learning technique has been introduced by the authors for hyperspectral plume detection/classification. The SVM-based ensemble learning consists of a number of SVM classifiers and the decisions from these sub-classifiers are combined to generate a final ensemble decision. The SVM-based ensemble technique first randomly selects spectral feature subspaces from the input data. Each individual classifier then independently conducts its own learning within its corresponding spectral feature space. Each classifier constitutes a weak classifier. These weak classifiers are combined to make an ensemble decision. The ensemble learning technique provides better performance than the conventional single SVM in terms of error rate. Various aggregating techniques like bagging, boosting, majority voting and weighted averaging were used to combine the weak classifiers, of which majority voting was found to be most robust. Yet, the ensemble of SVMs is suboptimal. Techniques that optimally weight the individual decisions from the sub-classifiers are strongly desirable to improve ensemble learning performance. In the proposed work, a recently introduced kernel learning technique called Multiple Kernel Learning (MKL) is used to optimally weight the kernel matrices of the sub-SVM classifiers. MKL basically iteratively performs l2 optimization on the Euclidian norm of the normal vector of the separating hyperplane between the classes (background and chemical plume) defined by the weighted kernel matrix followed by gradient descent optimization on the l1 regularized weighting coefficients of the individual kernel matrices. Due to l1 regularization on the weighting coefficients, the optimized weighting coefficients become sparse. The proposed work utilizes the sparse weighting coefficients to combine decision results of the SVM-based ensemble technique. A performance comparison between the aggregating techniques - MKL and majority voting as applied to hyperspectral chemical plume detection is presented in the paper.

Citation Download Citation

Prudhvi Gurram and Heesung Kwon "Ensemble learning based on multiple kernel learning for hyperspectral chemical plume detection", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951U (12 May 2010); https://doi.org/10.1117/12.850101

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