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Sparse Projection Matrix Approximation and Its Applications
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DOI：10.1109/LSP.2024.3519459.png)
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Abstract
En 中文
This letter introduces a sparse regularized projection matrix approximation (SPMA) model to recover cluster structures from affinity matrices. The model is formulated as a projection approximation problem with an entry-wise sparsity penalty to encourage sparse solutions. We propose two algorithms to solve this problem: one involves direct optimization on the Stiefel manifold using the Cayley transformation, while the other employs the Alternating Direction Method of Multipliers (ADMM). Numerical experiments on synthetic and real-world datasets demonstrate that our regularized projection matrix approximation approach significantly outperforms state-of-the-art methods in clustering accuracy and performance.
Keywords:
Sparse matrices
Manifolds
Approximation algorithms
Signal processing algorithms
Optimization
Convex functions
Matrix decomposition
Human activity recognition
Eigenvalues and eigenfunctions
Clustering algorithms
ADMM
clustering
regularisation
Stiefel manifold
sparse projection matrix approximation
Journal
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9.6
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1.1W
Citations:
1.7W