Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present a novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR).  extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph ight matrix and low-dimensional representations of data. That is, graph construction and low-rank matrix factorization are incorporated into a unified framework, which results in an automatically updated graph rather than a predefined one. The experimental results on some data sets demonstrate that the proposed algorithm outperforms the state-of-the-art low-rank matrix factorization methods.
['Gui-Fu Lu', 'Yong Wang', 'Jian Zou']
I am writing a research paper on a idea Low-Rank Matrix Factorization With Adaptive Graph Regularizer
[{"from": "human", "value": "I am writing a research paper on a idea Low-Rank Matrix Factorization With Adaptive Graph Regularizer"}, {"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 novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR).  extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph ight matrix and low-dimensional representations of data. That is, graph construction and low-rank matrix factorization are incorporated into a unified framework, which results in an automatically updated graph rather than a predefined one. The experimental results on some data sets demonstrate that the proposed algorithm outperforms the state-of-the-art low-rank matrix factorization methods."}]