Wang Qi, Qin Zequn, Nie Feiping, Li Xuelong
IEEE Trans Neural Netw Learn Syst. 2019 Apr;30(4):1265-1271. doi: 10.1109/TNNLS.2018.2861209. Epub 2018 Aug 20.
Spectral clustering has been widely used in various aspects, especially the machine learning fields. Clustering with similarity matrix and low-dimensional representation of data is the main reason of its promising performance shown in spectral clustering. However, such similarity matrix and low-dimensional representation directly derived from input data may not always hold when the data are high dimensional and has complex distribution. First, the similarity matrix simply based on the distance measurement might not be suitable for all kinds of data. Second, the low-dimensional representation might not be able to reflect the manifold structure of the original data. In this brief, we propose a novel linear space embedded clustering method, which uses adaptive neighbors to address the above-mentioned problems. Linearity regularization is used to make the data representation a linear embedded spectral. We also use adaptive neighbors to optimize the similarity matrix and clustering results simultaneously. Extensive experimental results show promising performance compared with the other state-of-the-art algorithms.
谱聚类已在各个方面得到广泛应用，尤其是在机器学习领域。基于相似性矩阵和数据的低维表示进行聚类是谱聚类展现出良好性能的主要原因。然而，当数据是高维且具有复杂分布时，直接从输入数据导出的这种相似性矩阵和低维表示可能并不总是成立。首先，仅基于距离度量的相似性矩阵可能并不适用于所有类型的数据。其次，低维表示可能无法反映原始数据的流形结构。在本简报中，我们提出了一种新颖的线性空间嵌入聚类方法，该方法使用自适应邻域来解决上述问题。线性正则化用于使数据表示成为线性嵌入谱。我们还使用自适应邻域来同时优化相似性矩阵和聚类结果。与其他现有最先进算法相比，大量实验结果显示出良好的性能。
