Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Several models have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). The AGR approach significantly accelerates graph-based learning by exploring a set of anchors. Hover, when a dataset becomes much larger, AGR still faces a big graph which brings dramatically increasing computational costs. To overcome this issue,  propose a novel Hierarchical Anchor Graph Regularization (HAGR) approach by exploring multiple-layer anchors with a pyramid-style structure. In HAGR, the labels of datapoints are inferred from the coarsest anchors layer by layer in a coarse-to-fine manner. The label smoothness regularization is performed on all datapoints, and  demonstrate that the optimization process only involves a small-size reduced Laplacian matrix.  also introduce a fast approach to construct our hierarchical anchor graph based on an approximate nearest neighbor search technique. Experiments on million-scale datasets demonstrate the effectiveness and efficiency of the proposed HAGR approach over existing methods. Results show that the HAGR approach is even able to achieve a good performance within 3 minutes in an 8-million-example classification task.
['Meng Wang', 'Weijie Fu', 'Shijie Hao', 'Hengchang Liu', 'Xindong Wu']
I am writing a research paper on a idea Learning on Big Graph: Label Inference and Regularization with Anchor Hierarchy
[{"from": "human", "value": "I am writing a research paper on a idea Learning on Big Graph: Label Inference and Regularization with Anchor Hierarchy"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Several models have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). The AGR approach significantly accelerates graph-based learning by exploring a set of anchors. Hover, when a dataset becomes much larger, AGR still faces a big graph which brings dramatically increasing computational costs. To overcome this issue,  propose a novel Hierarchical Anchor Graph Regularization (HAGR) approach by exploring multiple-layer anchors with a pyramid-style structure. In HAGR, the labels of datapoints are inferred from the coarsest anchors layer by layer in a coarse-to-fine manner. The label smoothness regularization is performed on all datapoints, and  demonstrate that the optimization process only involves a small-size reduced Laplacian matrix.  also introduce a fast approach to construct our hierarchical anchor graph based on an approximate nearest neighbor search technique. Experiments on million-scale datasets demonstrate the effectiveness and efficiency of the proposed HAGR approach over existing methods. Results show that the HAGR approach is even able to achieve a good performance within 3 minutes in an 8-million-example classification task."}]