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Xiuyan Ni

2 accepted papers

2019

A Topological Regularizer for Classifiers via Persistent Homology

AISTATS 2019poster

Regularization plays a crucial role in supervised learning. Most existing methods enforce a global regularization in a structure agnostic manner. In this paper, we initiate a new direction and propose to enforce the structural simplicity of the classification boundary by regularizing over its topolo…

Cited by 156SourcePDFScholar
2017

Composing Tree Graphical Models with Persistent Homology Features for Clustering Mixed-Type Data

ICML 2017poster

Clustering data with both continuous and discrete attributes is a challenging task. Existing methods lack a principled probabilistic formulation. In this paper, we propose a clustering method based on a tree-structured graphical model to describe the generation process of mixed-type data. Our tree-s…

Cited by 25SourcePDFScholar