Inv-SENnet: Invariant Self Expression Network for clustering under biased data
This addresses the issue of biased clustering outputs in data-exploration tasks for natural sciences, offering an incremental improvement over existing methods.
The paper tackles the problem of subspace clustering failing to handle unwanted biases in datasets, proposing a framework that jointly removes biases and clusters data, with experimental results demonstrating its effectiveness.
Subspace clustering algorithms are used for understanding the cluster structure that explains the dataset well. These methods are extensively used for data-exploration tasks in various areas of Natural Sciences. However, most of these methods fail to handle unwanted biases in datasets. For datasets where a data sample represents multiple attributes, naively applying any clustering approach can result in undesired output. To this end, we propose a novel framework for jointly removing unwanted attributes (biases) while learning to cluster data points in individual subspaces. Assuming we have information about the bias, we regularize the clustering method by adversarially learning to minimize the mutual information between the data and the unwanted attributes. Our experimental result on synthetic and real-world datasets demonstrate the effectiveness of our approach.