Sep 17, 2016 · We propose a novel GSSL based on a batch of informative beacons with sparsity appropriately harnessed, rather than constructing the pairwise affinity graph.
Abstract. Graph-based Semi-Supervised Learning (GSSL) has limita- tions in widespread applicability due to its computationally prohibitive.
In this work, we propose a flexible generative framework for graph-based semi-supervised learning, which approaches the joint distribution of the node features, ...
Efficient and robust semi-supervised learning over a sparse- regularized graph. (2016) Lecture Notes in Computer Science (including subseries Lecture. Notes ...
Experimental results on real datasets validate that our algorithm is effective and efficient to implement scalable inference, robust to sample corruptions, and ...
Bibliographic details on Efficient and Robust Semi-supervised Learning Over a Sparse-Regularized Graph.
In this paper, we propose a robust Graph-based Semi-Supervised Sparse Feature Selection (GS 3 FS) method based on the mixed convex and non-convex l 2,p -norm ( ...
Jun 12, 2023 · We further show how to approximately learn the best graphs from the sparse families efficiently using the conjugate gradient method. Our ...
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Jun 18, 2024 · We present a novel unified framework robust structure-aware semi-supervised learning called Unified RSSL (URSSL) for batch processing and recursive processing.
We employ the conjugate gradient method to compute fast, approximate inverses and optimize over new multi-parameter graph families that include sparse graphs.