Back to Search
Start Over
Multiple Flat Projections for Cross-Manifold Clustering
- Source :
- IEEE Transactions on Cybernetics. 52:7704-7718
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of the cross-manifold structures. In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems. In our MFPC, the given samples are projected into multiple subspaces to discover the global structures of the implicit manifolds. Thus, the cross-manifold clusters are distinguished from the various projections. Further, our MFPC is extended to nonlinear manifold clustering via kernel tricks to deal with more complex cross-manifold clustering. A series of non-convex matrix optimization problems in MFPC are solved by a proposed recursive algorithm. The synthetic tests show that our MFPC works on the cross-manifold structures well. Moreover, experimental results on the benchmark datasets show the excellent performance of our MFPC compared with some state-of-the-art clustering methods.<br />Comment: 12 pages, 58 figures
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Machine Learning (stat.ML)
02 engineering and technology
Machine Learning (cs.LG)
Statistics - Machine Learning
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Cluster analysis
Projection (set theory)
Mathematics::Symplectic Geometry
Series (mathematics)
Manifold
Computer Science Applications
Human-Computer Interaction
Nonlinear system
ComputingMethodologies_PATTERNRECOGNITION
Kernel (image processing)
Control and Systems Engineering
Video tracking
Benchmark (computing)
020201 artificial intelligence & image processing
Mathematics::Differential Geometry
Algorithm
Software
Information Systems
Subjects
Details
- ISSN :
- 21682275 and 21682267
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cybernetics
- Accession number :
- edsair.doi.dedup.....dd536ee9d3dd0fce473dd0bba9cb06fd