Optimized feature extraction by immune clonal selection algorithm

A new method of feature extraction based on immune clonal selection algorithm is proposed, in which the immune clonal selection algorithm is used to optimize the projection vector. Some orthogonal bases are randomly selected as the initial basis vector sets from the original feature space, and the direction of the basis vectors is optimized to generate the optimal projection vector using the immune clonal selection algorithm. This method provides a new scheme of applying the immune clonal algorithm to feature extraction. Experimental results on benchmark datasets and MSTAR dataset for SAR target recognition verify the effectiveness of the proposed method.

[1]  Boonserm Kijsirikul,et al.  A unified semi-supervised dimensionality reduction framework for manifold learning , 2008, Neurocomputing.

[2]  Giuseppe Nicosia,et al.  An Advanced Clonal Selection Algorithm with Ad-Hoc Network-Based Hypermutation Operators for Synthesis of Topology and Sizing of Analog Electrical Circuits , 2008, ICARIS.

[3]  Jong-Sen Lee,et al.  Principal components transformation of multifrequency polarimetric SAR imagery , 1992, IEEE Trans. Geosci. Remote. Sens..

[4]  Minghui Wang,et al.  Micro-displacement Super-resolution Based on Video Image Restoration , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[5]  Liangpei Zhang,et al.  Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[6]  G. Baudat,et al.  Feature vector selection and projection using kernels , 2003, Neurocomputing.

[7]  I K Fodor,et al.  A Survey of Dimension Reduction Techniques , 2002 .

[8]  Chulhee Lee,et al.  Optimizing feature extraction for multiclass problems , 2001, IEEE Trans. Geosci. Remote. Sens..

[9]  Vincenzo Cutello,et al.  An Immune Algorithm for Protein Structure Prediction on Lattice Models , 2007, IEEE Transactions on Evolutionary Computation.

[10]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  G.A. Rovithakis,et al.  A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Vincenzo Cutello,et al.  Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials , 2005, ICARIS.

[13]  Yang Ruliang,et al.  SAR Target Recognition Based on MRF and Gabor Wavelet Feature Extraction , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Gangyao Kuang,et al.  A Fast SAR Target Recognition Approach Using PCA Features , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[15]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[17]  L. Jiao,et al.  Immune secondary response and clonal selection inspired optimizers , 2009 .

[18]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .