Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The kernel methods play a pivotal role in machine learning algorithms. Unfortunately, working with the kernel methods must deal with an n times n kernel matrix, which is memory intensive. ,  present a parallel, approximate matrix factorization algorithm, which loads only essential data to individual processors to enable parallel processing of data. Our method reduces space requirement for the kernel matrix from O(n 2 ) to O(np/m), where n is the amount of data, p the reduced matrix dimension (p << n), and m the number of processors.
['Kaihua Zhu', 'Hang Cui', 'Hongjie Bai', 'Jian Li', 'Zhihuan Qiu', 'Hao Wang', 'Hui Xu', 'Edward Yi Chang']
I am writing a research paper on a idea Parallel Approximate Matrix Factorization for Kernel Methods
[{"from": "human", "value": "I am writing a research paper on a idea Parallel Approximate Matrix Factorization for Kernel Methods"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The kernel methods play a pivotal role in machine learning algorithms. Unfortunately, working with the kernel methods must deal with an n times n kernel matrix, which is memory intensive. ,  present a parallel, approximate matrix factorization algorithm, which loads only essential data to individual processors to enable parallel processing of data. Our method reduces space requirement for the kernel matrix from O(n 2 ) to O(np/m), where n is the amount of data, p the reduced matrix dimension (p << n), and m the number of processors."}]