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Subspace clustering algorithms are used for understanding the clusterstructure that explains the dataset well. These methods are extensively usedfor data-exploration tasks in various areas of Natural Sciences. However, mostof these methods fail to handle unwanted biases in datasets. For datasets wherea data sample represents multiple attributes, naively applying any clusteringapproach can result in undesired output. To this end, we propose a novelframework for jointly removing unwanted attributes (biases) while learning tocluster data points in individual subspaces. Assuming we have information aboutthe bias, we regularize the clustering method by adversarially learning tominimize the mutual information between the data and the unwanted attributes.Our experimental result on synthetic and real-world datasets demonstrate theeffectiveness of our approach.

Inv-SENnet: Invariant Self Expression Network for clustering under biased data