Sanitized Clustering against Confounding Bias
Yinghua Yao Yuangang Pan Jing Li Ivor W. Tsang Xin Yao
Real-world datasets inevitably contain biases that arise from different
sources or conditions during data collection. Consequently, such inconsistency
itself acts as a confounding factor that disturbs the cluster analysis.
Existing methods eliminate the biases by projecting data onto the orthogonal
complement of the subspace expanded by the confounding factor before
clustering. Therein, the interested clustering factor and the confounding
factor are coarsely considered in the raw feature space, where the correlation
between the data and the confounding factor is ideally assumed to be linear for
convenient solutions. These approaches are thus limited in scope as the data in
real applications is usually complex and non-linearly correlated with the
confounding factor. This paper presents a new clustering framework named
Sanitized Clustering Against confounding Bias (SCAB), which removes the
confounding factor in the semantic latent space of complex data through a
non-linear dependence measure. To be specific, we eliminate the bias
information in the latent space by minimizing the mutual information between
the confounding factor and the latent representation delivered by Variational
Auto-Encoder (VAE). Meanwhile, a clustering module is introduced to cluster
over the purified latent representations. Extensive experiments on complex
datasets demonstrate that our SCAB achieves a significant gain in clustering
performance by removing the confounding bias. The code is available at
\url{https://github.com/EvaFlower/SCAB}.