Stratified Non-Negative Tensor Factorization

• URL: http://arxiv.org/abs/2411.18805v1
• Date: Wed, 27 Nov 2024 23:16:00 GMT
• Title: Stratified Non-Negative Tensor Factorization
• Authors: Alexander Sietsema, Zerrin Vural, James Chapman, Yotam Yaniv, Deanna Needell,
• Abstract summary: Stratified-NTF can identify interpretable topics with lower memory requirements than Stratified-NMF. We develop a multiplicative update rule and demonstrate the method on text and image data.
• Score: 45.439685980328605
• License:
• Abstract: Non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) decompose non-negative high-dimensional data into non-negative low-rank components. NMF and NTF methods are popular for their intrinsic interpretability and effectiveness on large-scale data. Recent work developed Stratified-NMF, which applies NMF to regimes where data may come from different sources (strata) with different underlying distributions, and seeks to recover both strata-dependent information and global topics shared across strata. Applying Stratified-NMF to multi-modal data requires flattening across modes, and therefore loses geometric structure contained implicitly within the tensor. To address this problem, we extend Stratified-NMF to the tensor setting by developing a multiplicative update rule and demonstrating the method on text and image data. We find that Stratified-NTF can identify interpretable topics with lower memory requirements than Stratified-NMF. We also introduce a regularized version of the method and demonstrate its effects on image data.

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