Paper proposes AMSRE for multi-view data reduction.
problem Enhance performances of multi-view data tasks.
method Auto-weighted Multi-view Sparse Reconstructive Embedding (AMSRE).
result AMSRE effectively reduces multi-view data dimensions.
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RFA-LCF improves clustering accuracy by robustly handling noise and errors.
problem Inaccurate representation and clustering results due to noise and hard constraints.
method Integrates robust flexible CF, sparse local-coordinate coding, and adaptive weighting into a unified model.
result Delivers state-of-the-art clustering results on public databases.
Meta Additive Model learns auto-weighting for robust sparse learning.
problem Sparse models' sensitivity to atypical data and noise.
method Bilevel optimization framework with meta data-driven weighting.
result MAM outperforms state-of-the-art models under various data corruptions.
A new method improves graph-based semi-supervised classification by removing noise and mixed signs.
problem Inaccurate soft labels and noise in graph-based semi-supervised classification.
method Triple-matrix-recovery-based robust auto-weighted label propagation framework (ALP-TMR).
result Improved robustness to noise and outliers in label estimation.
Scalable and robust TR decomposition for large-scale data with missing entries and outliers.
problem Handling large-scale tensor data with missing entries and outliers.
method Auto-weighted steepest descent method for missing entries and outliers identification, FGMC and RStS strategies.
result Outperforms existing TR decomposition methods in the presence of outliers and runs faster than robust tensor completion algorithms.