# Sequence Graph Transform (SGT): A Feature Embedding Function for Sequence Data Mining

arXiv: https://arxiv.org/abs/1608.03533 · HTML: https://mlsanity.com/paper/1608.03533/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2016.

## Built on by (most cited first, 5 of 5)

- [A Framework for Understanding and Visualizing Strategies of RL Agents](https://mlsanity.com/paper/2208.08552/) (2022)
- [nTreeClus: a Tree-based Sequence Encoder for Clustering Categorical Series](https://mlsanity.com/paper/2102.10252/) (2021)
- [Deep Visual-Genetic Biometrics for Taxonomic Classification of Rare Species](https://mlsanity.com/paper/2305.06695/) (2023)
- [Interpretable Sequence Clustering](https://mlsanity.com/paper/2309.01140/) (2023)
- [Hamming Encoder: Mining Discriminative k-mers for Discrete Sequence Classification](https://mlsanity.com/paper/2310.10321/) (2023)

## Abstract (arXiv)

Sequence feature embedding is a challenging task due to the unstructuredness of sequence, i.e., arbitrary strings of arbitrary length. Existing methods are efficient in extracting short-term dependencies but typically suffer from computation issues for the long-term. Sequence Graph Transform (SGT), a feature embedding function, that can extract a varying amount of short- to long-term dependencies without increasing the computation is proposed. SGT's properties are analytically proved for interpretation under normal and uniform distribution assumptions. SGT features yield significantly superior results in sequence clustering and classification with higher accuracy and lower computation as compared to the existing methods, including the state-of-the-art sequence/string Kernels and LSTM.