| Li Wenfa (李文法),Wang Gongming,Li Ke,Huang Su.[J].高技术通讯(英文),2017,23(2):179~184 |
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| Similarity measurement method of high-dimensional data based on normalized net lattice subspace |
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| DOI：10.3772/j.issn.1006-6748.2017.02.009 |
| 中文关键词: |
| 英文关键词: high-dimensional data, the curse of dimensionality, similarity, normalization, subspace, Npsim |
| 基金项目: |
| Author Name | Affiliation | | Li Wenfa (李文法) | | | Wang Gongming | | | Li Ke | | | Huang Su | |
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| 中文摘要: |
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| 英文摘要: |
| The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data. The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity, leading to the dissimilarities between any results. A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed. The data range of each dimension is divided into several intervals, and the components in different dimensions are mapped onto the corresponding interval. Only the component in the same or adjacent interval is used to calculate the similarity. To validate this method, three data types are used, and seven common similarity measurement methods are compared. The experimental result indicates that the relative difference of the method is increasing with the dimensionality and is approximately two or three orders of magnitude higher than the conventional method. In addition, the similarity range of this method in different dimensions is ［0, 1］, which is fit for similarity analysis after dimensionality reduction. |
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