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A novel improved linear discriminant analysis (ILDA) method is presented. Comparing with LDA, under the condition of d < c -1, d and c are the dimensionality of feature subspace and the number of classes respectively, ILDA uniformly preserves the class distances of classpairs by rearranging the contribution of each class-pair to the generalized between-class scatter matrix after whitening within-class...
In this paper, we propose a new near-field source localization algorithm without any spectral peak searching or parameter pairing. Firstly, based on fourth-order cumulant of the outputs of a uniform linear array (ULA), we derive a multiple invariance-sensor array processing (MI-SAP)-type data model using the formed cumulant matrices. Secondly, the DOA, range, and frequency parameters are estimated...
In this paper, we propose a new near-field source localization algorithm without any spectral peak searching or parameter pairing. Firstly, based on fourth-order cumulant of the outputs of a uniform linear array (ULA), we derive a multiple invariance-sensor array processing (MI-SAP)-type data model using the formed cumulant matrices. Secondly, the DOA, range, and frequency parameters are estimated...
Multilayer perceptrons offer an integrated procedure for feature extraction and Bayes classification by learning the decision boundary. Its feedforward autoassociative architecture can also be used to construct subspaces in a supervised or unsupervised model [A.K. Jain et al., 2000]. On the other hand, multiclass linear discriminant analysis provides a multivariate prediction by estimating the density...
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