TL;DR
A Large-scale Structured Subspace Clustering (LSSC) framework that integrates anchor-based reconstruction, locality-aware coefficient initialization, distance-weighted structure regularization, and coefficient regularization to learn a compact nonnegative sample-to-anchor representation is proposed, thereby providing favorable scalability on large datasets.
ELSS learns an explicit and nonlinear low-rank subspace within a graph-structured embedding space, effectively un-covering latent cluster structures and introduces a homophily-aware adaptive graph filter, which dynamically calibrates smoothing intensity to preserve discriminative ego-information.
A flexible encoder based on low-rank embedding that adaptively captures the most informative components in the feature space of the input data while filtering out redundant or noisy information, effectively alleviating over-connected affinities is proposed.
It is shown that approximate $K$-means applied to the multi-kernel spectral embedding achieves exact recovery with high probability, which is more informative than conventional global eigenspace perturbation estimates.
This paper proposes a feature-graph-guided adaptive Log-L2,1 sparse NMF with anchor dual graphs under a logarithmic framework that jointly integrate sample structure preservation, feature structure preservation, and feature-aware sparse learning within a unified graph-NMF model.
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