聚类分析
人工智能
计算机科学
光谱聚类
高光谱成像
噪音(视频)
秩(图论)
模式识别(心理学)
张量(固有定义)
高斯噪声
代表(政治)
数据挖掘
数学
图像(数学)
组合数学
政治
法学
纯数学
政治学
作者
Xintong Zou,Yunjie Zhang,Yanrong Yang
标识
DOI:10.1145/3584871.3584897
摘要
Multi-view Spectral Clustering (MVSC) is a hot research direction in computer vision and machine learning. In recent years, scholars have proposed many MVSC methods based on tensor low rank representation. However, most of them are more suitable for processing noiseless data, but not ideal for noisy data. Inspired by the noise representation idea of hyperspectral noise images, this paper proposes a robust low rank tensor MVSC method for Gaussian and salt and pepper noise data based on MVSC-TLRN method. Similar to MVSC-TLRN method, the proposed method represents the multi-view clustering problem of noise data as a low rank tensor learning problem, which is solved by inexact augmented Lagrangian method. The experimental results on five image datasets and two document datasets show that the proposed method is much better than the existing methods.
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