Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification.  proposes a novel non-negative low-rank and sparse (NNLRS) graph for semi-supervised learning. The ights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative.  demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis. Extensive experiments testify to the significant advantages of NNLRS-graph over graphs obtained through conventional means.
['Liansheng Zhuang', 'Haoyuan Gao', 'Zhouchen Lin', 'Yi Ma', 'Xin Zhang', 'Nenghai Yu']
I am writing a research paper on a idea Non-negative low rank and sparse graph for semi-supervised learning
[{"from": "human", "value": "I am writing a research paper on a idea Non-negative low rank and sparse graph for semi-supervised learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification.  proposes a novel non-negative low-rank and sparse (NNLRS) graph for semi-supervised learning. The ights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative.  demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis. Extensive experiments testify to the significant advantages of NNLRS-graph over graphs obtained through conventional means."}]