Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new algorithm for minimizing a convex loss-function subject to regularization. Our framework applies to numerous problems in machine learning and statistics; notably, for sparsity-promoting regularizers such as ‘1 or ‘1;1 norms, it enables efficient computation of sparse solutions. Our approach is based on the trust-region framework with nonsmooth objectives, which allows us to build on known results to provide convergence analysis.  avoid the computational overheads associated with the conventional Hessian approximation used by trust-region methods by instead using a simple separable quadratic approximation. This approximation also enables use of proximity operators for tackling nonsmooth regularizers.  illustrate the versatility of our resulting algorithm by specializing it to three mixed-norm regression problems: group lasso [36], group logistic regression [21], and multi-task lasso [19].  experiment with both synthetic and real-world large-scale data—our method is seen to be competitive, robust, and scalable.
['Dongmin Kim', 'Suvrit Sra', 'Inderjit S. Dhillon']
I am writing a research paper on a idea A scalable trust-region algorithm with application to mixed-norm regression
[{"from": "human", "value": "I am writing a research paper on a idea A scalable trust-region algorithm with application to mixed-norm regression"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new algorithm for minimizing a convex loss-function subject to regularization. Our framework applies to numerous problems in machine learning and statistics; notably, for sparsity-promoting regularizers such as \u20181 or \u20181;1 norms, it enables efficient computation of sparse solutions. Our approach is based on the trust-region framework with nonsmooth objectives, which allows us to build on known results to provide convergence analysis.  avoid the computational overheads associated with the conventional Hessian approximation used by trust-region methods by instead using a simple separable quadratic approximation. This approximation also enables use of proximity operators for tackling nonsmooth regularizers.  illustrate the versatility of our resulting algorithm by specializing it to three mixed-norm regression problems: group lasso [36], group logistic regression [21], and multi-task lasso [19].  experiment with both synthetic and real-world large-scale data\u2014our method is seen to be competitive, robust, and scalable."}]