Robust semi-supervised non-negative matrix factorization for binary subspace learning
Robust semi-supervised non-negative matrix factorization for binary subspace learning
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DOI：
10.1007/s40747-021-00285-1
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发表时间：
2021-02
影响因子：
5.8
通讯作者：
Xiangguang Dai;Keke Zhang;Juntang Li;Jiang Xiong;Nian Zhang;Huaqing Li
中科院分区：
文献类型：
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作者：
Xiangguang Dai;Keke Zhang;Juntang Li;Jiang Xiong;Nian Zhang;Huaqing Li
非负矩阵分解及其扩展被应用于各种领域(即降维、聚类等)。当原始数据受到离群点和噪声的干扰时，大多数非负矩阵分解方法不能实现稳健的因式分解，无法通过二进制码学习一个子空间。提出了一种用于图像聚类的稳健的半监督非负矩阵分解二元子空间学习方法RSNMF。为了在被离群点和噪声污染的数据集上获得更好的聚类性能，我们提出了对噪声矩阵的加权约束，并将流形学习引入非负矩阵分解。此外，我们利用离散哈希学习方法对学习到的子空间进行约束，可以从原始数据得到二进制子空间。实验结果验证了RSNMF在被椒盐噪声和连续遮挡污染的人脸数据集上的二元子空间学习和图像聚类中的鲁棒性和有效性。
Non-negative matrix factorization and its extensions were applied to various areas (i.e., dimensionality reduction, clustering, etc.). When the original data are corrupted by outliers and noise, most of non-negative matrix factorization methods cannot achieve robust factorization and learn a subspace with binary codes. This paper puts forward a robust semi-supervised non-negative matrix factorization method for binary subspace learning, called RSNMF, for image clustering. For better clustering performance on the dataset contaminated by outliers and noise, we propose a weighted constraint on the noise matrix and impose manifold learning into non-negative matrix factorization. Moreover, we utilize the discrete hashing learning method to constrain the learned subspace, which can achieve a binary subspace from the original data. Experimental results validate the robustness and effectiveness of RSNMF in binary subspace learning and image clustering on the face dataset corrupted by Salt and Pepper noise and Contiguous Occlusion.