# Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework

arXiv: https://arxiv.org/abs/2211.02883 · HTML: https://mlsanity.com/paper/2211.02883/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Collecting and Analyzing Multidimensional Data with Local Differential Privacy](https://mlsanity.com/paper/1907.00782/) (2019)
- [Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank](https://mlsanity.com/paper/1906.06826/) (2019)
- [A generalized power iteration method for solving quadratic problem on the Stiefel manifold](https://mlsanity.com/paper/1701.00381/) (2017)
- [Efficient Estimation of Heat Kernel PageRank for Local Clustering](https://mlsanity.com/paper/1904.02707/) (2019)
- [PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs](https://mlsanity.com/paper/1905.02354/) (2019)

## Abstract (arXiv)

Spectral clustering is an effective methodology for unsupervised learning. Most traditional spectral clustering algorithms involve a separate two-step procedure and apply the transformed new representations for the final clustering results. Recently, much progress has been made to utilize the non-negative feature property in real-world data and to jointly learn the representation and clustering results. However, to our knowledge, no previous work considers a unified model that incorporates the important multi-view information with those properties, which severely limits the performance of existing methods. In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC). Then, we derive an effective three-stage iterative solution for the proposed model and provide analytic solutions for the three sub-problems from the three stages. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features. Extensive experiments on three benchmark data sets demonstrate the effectiveness of the proposed method.