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Efficient spectral embedding representation approximation for large-scale data clustering
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AI
DOI：10.1016/j.patcog.2025.112693.png)
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Abstract
En 中文
• An efficient approximate spectral embedding representation (ASER) method for large-scale data is presented. • The spectral embedding representations of the anchors are directly used to approximate those of the original samples. • ASER conducts spectral representation approximation in the embedding space instead of approximating the similarity matrix. • Extensive experimental results on several datasets show the effectiveness of ASER.
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