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Robust locality regularized non-negative matrix factorization with structure preservation for image classification
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DOI：10.1016/j.patcog.2025.112241.png)
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Abstract
En 中文
Despite the empirical success of non-negative matrix factorization (NMF) methods based on graph embedding, there are still some deficiencies, such as: 1) They are sensitive to affinity matrix due to encoding inherent structure by a self-defined graph cannot perfectly accommodate different underlying distributions of various datasets; 2) There is no comprehensive consideration of global and local structures; 3) The structural consistency of the feature space, the representation space and the original space are not persisted. Hence, we propose a novel model, i.e. robust locality regularized non-negative matrix factorization with structure preservation (RLNMF-SP), to tackle the above issues. Firstly, the low-rank attribute is applied to suppress the negative effects of noise and occlusion on the model, while obtaining a characterization of the global structure of the sample. Secondly, the Euclidean distance is employed to automatically assign appropriate neighborhoods to each data point, which reach to explicitly grasp the geometric topology without human intervention. Finally, graph embedding and positional constraint criteria are adopted to achieve structure preservation. Specifically, the learned similarity matrix is adopted to maintain the neighbor relationship invariant in the representation space, and hold the compactness within classes and separability between classes in the feature space. Numerous experiments have shown that this model exhibits excellent performance on interference-free, noise-containing, occlusion-containing, and mixed noise and occlusion datasets, and its recognition rate is on average 1–4 % higher than that of other models.
Keywords:
non-negative matrix factorization
graph embedding
structure preservation
robust locality regularization
feature space consistency
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