计算自适应的可变参数,用于权衡原始结构和扩散结构,这个主要是针对上述的多视图融合参数
α
\alpha
α,这个参数权衡了当前视图和其他视图的比例,当
α
\alpha
α较小时融合视图中当前视图的比重较大。论文中为了构造自适应的参数,使用了矩阵量连续点乘的方法,连乘的结果如下。根据连乘结果中非零元素的个数
N
~
\widetilde{N}
N来定
α
\alpha
α的大小。
α
=
1
−
N
~
/
N
2
\alpha=1-\widetilde{N}/N^2
α=1−N/N2:
最终将扩散后的多个视图图结构直接求和作为最终的图结构,模型图如下:
Evaluation
在七个聚类的指标上进行实验评估,多视图数据集如下:
Conclusion
论文将单个图的扩散公式扩展到多个视图上,取得了显著的效果。
Notes
Graph based multi-view clustering has been paid great attention by exploring the neighborhood relationship among data points from multiple views.
Extensive experiments on several benchmark datasets are conducted to demonstrate the effectiveness of the proposed method in terms of seven clustering evaluation metrics.
It is not uncommon that an object is usually described by multi-view features.
Multi-view clustering, which partitions these multi-view data into different groups by using the complementary information of multi-view feature sets to ensure that highly similar instances are divided into the same group, is an important branch of multi-view learning.
In general, most of previous multi-view clustering methods employ graph-based models since the similarity graph can characterize the data structure effectively.
In biomedical research, both the chemical structure and chemical response in different cells can be used to represent a certain drug, while the sequence and gene expression values can represent a certain protein in different aspects.
In general, most of previous multi-view clustering methods employ graph-based models since the similarity graph can characterize the data structure effectively.