Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Least Squares Support Vector Machines (LSSVM) perform classification using L2-norm on the ight vector and a squared loss function with linear constraints. The major advantage over classical L2-norm support vector machine (SVM) is that it solves a system of linear equations rather than solving a quadratic programming problem. The L2-norm penalty on the ight vectors is known to robustly select features. The zero-norm or the number of non-zero elements in a vector is an ideal quantity for feature selection. The L0-norm minimization is a computationally intractable problem. Hover, a convex relaxation to the direct zero-norm minimization was proposed recently. ,  propose a combination of L2-norm penalty and the convex relaxation of the L0-norm penalty for feature selection in classification problems.  propose a primal-dual framework for feature selection using the combination of L2-norm and L0-norm penalty resulting in closed form solution. A series of experiments on microarray data and UCI data demonstrates that our proposed method results in better performance.
['Raghvendra Mall', 'Johan A. K. Suykens', 'Mohammed El Anbari', 'Halima Bensmail']
I am writing a research paper on a idea Primal-dual framework for feature selection using least squares support vector machines
[{"from": "human", "value": "I am writing a research paper on a idea Primal-dual framework for feature selection using least squares support vector machines"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Least Squares Support Vector Machines (LSSVM) perform classification using L2-norm on the ight vector and a squared loss function with linear constraints. The major advantage over classical L2-norm support vector machine (SVM) is that it solves a system of linear equations rather than solving a quadratic programming problem. The L2-norm penalty on the ight vectors is known to robustly select features. The zero-norm or the number of non-zero elements in a vector is an ideal quantity for feature selection. The L0-norm minimization is a computationally intractable problem. Hover, a convex relaxation to the direct zero-norm minimization was proposed recently. ,  propose a combination of L2-norm penalty and the convex relaxation of the L0-norm penalty for feature selection in classification problems.  propose a primal-dual framework for feature selection using the combination of L2-norm and L0-norm penalty resulting in closed form solution. A series of experiments on microarray data and UCI data demonstrates that our proposed method results in better performance."}]