嵌入
子空间拓扑
聚类分析
代表(政治)
非线性降维
计算机科学
趋同(经济学)
歧管(流体力学)
图形
近似算法
图嵌入
理论计算机科学
特征学习
数学
人工智能
算法
降维
经济增长
机械工程
工程类
政治
政治学
法学
经济
作者
Danyang Wu,Feiping Nie,Rong Wang,Xuelong Li
标识
DOI:10.1109/icassp40776.2020.9053219
摘要
This paper tackles multi-view clustering via proposing a novel mixed embedding approximation (MEA) method. Formally, we aim to learn a uniform orthogonal embedding based on the orthogonal pre-embeddings of each view. At first, we hope that the uniform embedding can reconstruct the affinity graph of each view. To improve the representation of learnt embedding, we perform an embedding approximation on Grassmann manifold which is famous on subspace analysis. To perform the difference of views, a hidden weights learning module is provided. Moreover, we propose an iterative algorithm to solve the proposed MEA method and provide rigorously convergence analysis. Extensive experiments demonstrate the superiorities of the proposed method.
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