Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nonlinear data-driven dimensionality reduction techniques have recently gained popularity due to the emergence of high dimensional data sets. The algorithmic complexity and storage requirements of these techniques, hover, can make them prohibitive in resource-limited applications. It is therefore beneficial to reduce the number of exemplar samples required for performing an out-of-sample extension to a test point. ,  propose a novel method for selecting a minimal set of exemplars and performing the out-of-sample extension. In the case of two-class target recognition with synthetic aperture radar (SAR) data,  compare the efficacy of the proposed approach with other approaches for selecting a subset of the available training samples.  show that the proposed algorithm outperforms the existing methods by providing low-dimensional embeddings that maintain interclass separability using fer retained exemplars.
['Visar Berisha', 'Nitesh Shah', 'Donald E. Waagen', 'Harry A. Schmitt', 'Salvatore Bellofiore', 'Andreas Spanias', 'Douglas Cochran']
I am writing a research paper on a idea Sparse Manifold Learning with Applications to SAR Image Classification
[{"from": "human", "value": "I am writing a research paper on a idea Sparse Manifold Learning with Applications to SAR Image Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nonlinear data-driven dimensionality reduction techniques have recently gained popularity due to the emergence of high dimensional data sets. The algorithmic complexity and storage requirements of these techniques, hover, can make them prohibitive in resource-limited applications. It is therefore beneficial to reduce the number of exemplar samples required for performing an out-of-sample extension to a test point. ,  propose a novel method for selecting a minimal set of exemplars and performing the out-of-sample extension. In the case of two-class target recognition with synthetic aperture radar (SAR) data,  compare the efficacy of the proposed approach with other approaches for selecting a subset of the available training samples.  show that the proposed algorithm outperforms the existing methods by providing low-dimensional embeddings that maintain interclass separability using fer retained exemplars."}]