# Sparse Bilinear Logistic Regression

arXiv: https://arxiv.org/abs/1404.4104 · HTML: https://mlsanity.com/paper/1404.4104/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2014.

## Built on by (most cited first, 7 of 7)

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- [Disciplined Biconvex Programming](https://mlsanity.com/paper/2511.01813/) (2025)
- [A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression](https://mlsanity.com/paper/2105.14673/) (2021)
- [Structured Low-Rank Tensors for Generalized Linear Models](https://mlsanity.com/paper/2308.02922/) (2023)
- [Multidimensional Item Response Theory in the Style of Collaborative Filtering](https://mlsanity.com/paper/2301.00909/) (2023)
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## Abstract (arXiv)

In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems involving explanatory variables that are two-dimensional matrices. Such problems are common in computer vision, brain-computer interfaces, style/content factorization, and parallel factor analysis. The underlying optimization problem is bi-convex; we study its solution and develop an efficient algorithm based on block coordinate descent. We provide a theoretical guarantee for global convergence and estimate the asymptotical convergence rate using the Kurdyka-Lojasiewicz inequality. A range of experiments with simulated and real data demonstrate that sparse bilinear logistic regression outperforms current techniques in several important applications.