High-order correlation preserved incomplete multi-view subspace clustering method and system
US12393644B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 24, 2022 |
| Grant date | Aug 19, 2025 |
| Priority date | — |
| Expiry date | Jun 6, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F17/16
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A high-order correlation preserved incomplete multi-view subspace clustering method and system. The method comprises: S11, inputting an original data matrix, and converting original data into an observed part and an incomplete part; S12, obtaining a plurality of affinity matrices according to self-representation characteristics of the original data; S13, mining a high-order correlation between the plurality of affinity matrices by tensor factorization; S14, learning a unified affinity matrix from the plurality of affinity matrices, so as to obtain a global affinity matrix; S15, constructing a hypergraph on the basis of the global affinity matrix, and constraining an incomplete part of incomplete multi-view data by using a hypergraph-induced Laplacian matrix; S16, integrating the global affinity matrix, the tensor factorization and the hypergraph-induced Laplacian matrix constraint into a unified learning framework; S17, solving the obtained objective function by an alternating iterative optimization strategy; and S18, applying spectral clustering to the global affinity matrix.
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