Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many real-world objects described by multiple attributes or features can be decomposed as multiple "views" (e.g., an image can be described by a color view or a shape view), which often provides complementary information to each other. Learning a metric (similarity measures) for multi-view data is primary due to its wide applications in practices. Hover, leveraging multi-view information to produce a good metric is a great challenge and existing techniques are concerned with pairwise similarities, leading to undesirable fusion metric and high computational complexity. ,  propose a novel Metric Fusion technique via cross-view graph Random Walk, named MFRW, regarding a multi-view based similarity graphs (with each similarity graph constructed under each view). Instead of using pairwise similarities,  seek a high-order metric yielded by graph random walks over constructed similarity graphs. Observing that ``outlier views" may exist in the fusion process,  incorporate the coefficient matrices representing the correlation strength beten any two views into MFRW, named WMFRW. The principle of \textsf{WMFRW} is implemented by exploring the ``common latent structure" beten views. The empirical studies conducted on real-world databases demonstrate that our approach outperforms the state-of-the-art competitors in terms of effectiveness and efficiency.
['Yang Wang', 'Xuemin Lin', 'Qing Zhang']
I am writing a research paper on a idea Towards metric fusion on multi-view data: a cross-view based graph random walk approach
[{"from": "human", "value": "I am writing a research paper on a idea Towards metric fusion on multi-view data: a cross-view based graph random walk approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many real-world objects described by multiple attributes or features can be decomposed as multiple \"views\" (e.g., an image can be described by a color view or a shape view), which often provides complementary information to each other. Learning a metric (similarity measures) for multi-view data is primary due to its wide applications in practices. Hover, leveraging multi-view information to produce a good metric is a great challenge and existing techniques are concerned with pairwise similarities, leading to undesirable fusion metric and high computational complexity. ,  propose a novel Metric Fusion technique via cross-view graph Random Walk, named MFRW, regarding a multi-view based similarity graphs (with each similarity graph constructed under each view). Instead of using pairwise similarities,  seek a high-order metric yielded by graph random walks over constructed similarity graphs. Observing that ``outlier views\" may exist in the fusion process,  incorporate the coefficient matrices representing the correlation strength beten any two views into MFRW, named WMFRW. The principle of \\textsf{WMFRW} is implemented by exploring the ``common latent structure\" beten views. The empirical studies conducted on real-world databases demonstrate that our approach outperforms the state-of-the-art competitors in terms of effectiveness and efficiency."}]