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A SELF-ORGANIZING NEURAL NETWORK FOR NONLINEAR MAPPING OF DATA SETS

Platform : DSP

IEEE Projects Years : 1997

This paper appears in: Neural Networks, IEEE Transactions on Issue Date: Jan 1997 AIM: TO MAP DATA SETS VIA SELF ORGANIZING NEURAL NETWORKS ABSTRACT We present a new strategy called “curvilinear component analysis” (CCA) for dimensionality reduction and representation of multidimensional data sets. The principle of CCA is a self-organized neural network performing two tasks: vector quantization (VQ) of the submanifold in the data set (input space); and nonlinear projection (P) of these quantizing vectors toward an output space, providing a revealing unfolding of the submanifold. After learning, the network has the ability to continuously map any new point from one space into another: forward mapping of new points in the input space, or backward mapping of an arbitrary position in the output space LEARNING OBJECTIVE: To understand the ability of the network for continuously mapping of new point from one space into another INPUT: multidimensional data sets OUTPUT : Accessing the selected version of data sets. APPLICATIONS: Database utilization and management system. SOFTWARE TOOL USED: MATLAB 2007A

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