XAI beyond Classification: Interpretable Neural Clustering
- Publication Type:
- Journal Article
- Citation:
- Journal of Machine Learning Research, 2022, 23, (-)
- Issue Date:
- 2022-01-01
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In this paper we study two challenging problems in explainable AI XAI and data clustering The first is how to directly design a neural network with inherent interpretability rather than giving post hoc explanations of a black box model The second is implementing discrete k means with a differentiable neural network that embraces the advantages of parallel computing online clustering and clustering favorable representation learning To address these two challenges we design a novel neural network which is a differentiable reformulation of the vanilla k means called inTerpretable nEuraL cLustering TELL Our contributions are threefold First to the best of our knowledge most existing XAI works focus on supervised learning paradigms This work is one of the few XAI studies on unsupervised learning in particular data clustering Second TELL is an interpretable or the so called intrinsically explainable and transparent model In contrast most existing XAI studies resort to various means for understanding a black box model with post hoc explanations Third from the view of data clustering TELL possesses many properties highly desired by k means including but not limited to online clustering plug and play module parallel computing and provable convergence Extensive experiments show that our method achieves superior performance comparing with 14 clustering approaches on three challenging data sets The source code could be accessed at www pengxi me 2022 Xi Peng Yunfan Li Ivor W Tsang Hongyuan Zhu Jiancheng Lv and Joey Tianyi Zhou
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