Collaborative Learning with Unreliability Adaptation for Semi-Supervised Image Classification

Article Computer Science, Artificial Intelligence

Structured graph learning for clustering and semi-supervised classification

Zhao Kang et al.

Summary: Graph-based clustering and semi-supervised classification techniques have shown impressive performance in modeling structures and interactions. This paper proposes a graph learning framework that preserves both local and global structure of data, outperforming other state-of-the-art methods in extensive experiments. Theoretical analysis reveals the equivalence of the proposed model to a combination of kernel k-means and k-means methods under certain conditions.

PATTERN RECOGNITION (2021)

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Article Computer Science, Artificial Intelligence

Deep graph learning for semi-supervised classification

Guangfeng Lin et al.

Summary: Graph learning dynamically captures data distribution structure based on graph convolutional networks. The quality of learning the graph structure directly impacts semi-supervised classification using GCN. Existing methods combine computational layers and losses into GCN to explore global and local graphs, which have different roles in semi-supervised classification.

PATTERN RECOGNITION (2021)

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Article Computer Science, Artificial Intelligence

A simple graph-based semi-supervised learning approach for imbalanced classification

Jianjin Deng et al.

Summary: The study introduces a simple graph-based semi-supervised learning method that can effectively address various degrees of class imbalance and enhance the discriminative power of graph-based learning through estimating class proportions. Experimental results demonstrate promising performance of the approach in handling classification problems.

PATTERN RECOGNITION (2021)

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Article Computer Science, Artificial Intelligence