Stratified-NMF for Heterogeneous Data

• URL: http://arxiv.org/abs/2311.10789v1
• Date: Fri, 17 Nov 2023 00:34:41 GMT
• Title: Stratified-NMF for Heterogeneous Data
• Authors: James Chapman, Yotam Yaniv, Deanna Needell
• Abstract summary: We propose a modified NMF objective, Stratified-NMF, that simultaneously learns strata-dependent statistics and a shared topics matrix. We apply our method to three real world datasets and empirically investigate their learned features.
• Score: 8.174199227297514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-negative matrix factorization (NMF) is an important technique for obtaining low dimensional representations of datasets. However, classical NMF does not take into account data that is collected at different times or in different locations, which may exhibit heterogeneity. We resolve this problem by solving a modified NMF objective, Stratified-NMF, that simultaneously learns strata-dependent statistics and a shared topics matrix. We develop multiplicative update rules for this novel objective and prove convergence of the objective. Then, we experiment on synthetic data to demonstrate the efficiency and accuracy of the method. Lastly, we apply our method to three real world datasets and empirically investigate their learned features.

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