# Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking

arXiv: https://arxiv.org/abs/1807.08692 · HTML: https://mlsanity.com/paper/1807.08692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Learning Query Expansion over the Nearest Neighbor Graph](https://mlsanity.com/paper/2112.02666/) (2021)

## Abstract (arXiv)

State of the art image retrieval performance is achieved with CNN features and manifold ranking using a k-NN similarity graph that is pre-computed off-line. The two most successful existing approaches are temporal filtering, where manifold ranking amounts to solving a sparse linear system online, and spectral filtering, where eigen-decomposition of the adjacency matrix is performed off-line and then manifold ranking amounts to dot-product search online. The former suffers from expensive queries and the latter from significant space overhead. Here we introduce a novel, theoretically well-founded hybrid filtering approach allowing full control of the space-time trade-off between these two extremes. Experimentally, we verify that our hybrid method delivers results on par with the state of the art, with lower memory demands compared to spectral filtering approaches and faster compared to temporal filtering.