School of Telecommunications Engineering, Xidian University, Shaanxi 710071, China.
School of Telecommunications Engineering, Xidian University, Shaanxi 710071, China.
Neural Netw. 2023 Jul;164:408-418. doi: 10.1016/j.neunet.2023.04.044. Epub 2023 May 5.
Recently, there has been tremendous interest in developing graph-based subspace clustering in high-dimensional data, which does not require a priori knowledge of the number of dimensions and subspaces. The general steps of such algorithms are dictionary representation and spectral clustering. Traditional methods use the dataset itself as a dictionary when performing dictionary representation. There are some limitations that the redundant information present in the dictionary and features may make the constructed graph structure unclear and require post-processing to obtain labels. To address these problems, we propose a novel subspace clustering model that first introduces feature selection to process the input data, randomly selects some samples to construct a dictionary to remove redundant information and learns the optimal bipartite graph with K-connected components under the constraint of the (normalized) Laplacian rank. Finally, the labels are obtained directly from the graphs. The experimental results on motion segmentation and face recognition datasets demonstrate the superior effectiveness and stability of our algorithm.
最近，人们对在高维数据中开发基于图的子空间聚类产生了浓厚的兴趣，这种方法不需要先验的维度和子空间数量的知识。这类算法的一般步骤是字典表示和谱聚类。传统方法在进行字典表示时使用数据集本身作为字典。存在一些局限性，字典和特征中的冗余信息可能会使构建的图结构不清晰，需要进行后处理来获取标签。为了解决这些问题，我们提出了一种新的子空间聚类模型，该模型首先引入特征选择来处理输入数据，随机选择一些样本构建字典以去除冗余信息，并在（归一化）拉普拉斯秩约束下学习具有 K 连通分量的最优二分图。最后，直接从图中获取标签。运动分割和人脸识别数据集上的实验结果表明，我们的算法具有优越的有效性和稳定性。
