# Sparse Deep Nonnegative Matrix Factorization

arXiv: https://arxiv.org/abs/1707.09316 · HTML: https://mlsanity.com/paper/1707.09316/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2017.

## Built on by (most cited first, 3 of 3)

- [An Accelerated Block Proximal Framework with Adaptive Momentum for Nonconvex and Nonsmooth Optimization](https://mlsanity.com/paper/2308.12126/) (2023)
- [Input Guided Multiple Deconstruction Single Reconstruction neural network models for Matrix Factorization](https://mlsanity.com/paper/2405.13449/) (2024)
- [A Generalized Deep Non-negative Matrix Factorization Approach for SAR Automatic Target Recognition](https://mlsanity.com/paper/2607.09779/) (2026)

## Abstract (arXiv)

Nonnegative matrix factorization is a powerful technique to realize dimension reduction and pattern recognition through single-layer data representation learning. Deep learning, however, with its carefully designed hierarchical structure, is able to combine hidden features to form more representative features for pattern recognition. In this paper, we proposed sparse deep nonnegative matrix factorization models to analyze complex data for more accurate classification and better feature interpretation. Such models are designed to learn localized features or generate more discriminative representations for samples in distinct classes by imposing L1-norm penalty on the columns of certain factors. By extending one-layer model into multi-layer one with sparsity, we provided a hierarchical way to analyze big data and extract hidden features intuitively due to nonnegativity. We adopted the Nesterov's accelerated gradient algorithm to accelerate the computing process with the convergence rate of O(1/k2) after k steps iteration. We also analyzed the computing complexity of our framework to demonstrate their efficiency. To improve the performance of dealing with linearly inseparable data, we also considered to incorporate popular nonlinear functions into this framework and explored their performance. We applied our models onto two benchmarking image datasets, demonstrating our models can achieve competitive or better classification performance and produce intuitive interpretations compared with the typical NMF and competing multi-layer models.