In order to reduce the computational cost of dimensionality reduction of large-scale data sets,an orthogonal unsupervised graph embedding dimensionality reduction algorithm based on balanced hierarchical K-means is proposed.The necessary and sufficient conditions for locally preserving the equivalence of projection and spectral regression were obtained.An anchor generation strategy based on balanced hierarchical K-means was put forward,and a special similarity matrix was constructed to accelerate the process of local preserving projection.Combined with the orthogonal constraints,an orthogonal unsupervised large-scale graph embedding dimension reduction method is proposed.Experiments on several public data sets show that the proposed method can achieve efficient and fast dimensionality reduction for large-scale data sets.
关键词
数据降维/平衡分层K均值/局部保持投影/无监督大型图嵌入
Key words
Data dimension reduction/Balanced hierarchical K-means/Locality preserving projection/Unsupervised large graph embedding
维普
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