ORTHOGONAL UNSUPERVISED LARGE GRAPH EMBEDDING DIMENSION REDUCTION ALGORITHM BASED ON BALANCED HIERARCHICAL K-MEANS
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.
Data dimension reductionBalanced hierarchical K-meansLocality preserving projectionUnsupervised large graph embedding
万方数据
维普
