# Deep Hyperalignment

arXiv: https://arxiv.org/abs/1710.03923 · HTML: https://mlsanity.com/paper/1710.03923/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding](https://mlsanity.com/paper/2402.08994/) (2024)
- [Emotional Brain State Classification on fMRI Data Using Deep Residual and Convolutional Networks](https://mlsanity.com/paper/2210.17015/) (2022)

## Abstract (arXiv)

This paper proposes Deep Hyperalignment (DHA) as a regularized, deep extension, scalable Hyperalignment (HA) method, which is well-suited for applying functional alignment to fMRI datasets with nonlinearity, high-dimensionality (broad ROI), and a large number of subjects. Unlink previous methods, DHA is not limited by a restricted fixed kernel function. Further, it uses a parametric approach, rank-m Singular Value Decomposition (SVD), and stochastic gradient descent for optimization. Therefore, DHA has a suitable time complexity for large datasets, and DHA does not require the training data when it computes the functional alignment for a new subject. Experimental studies on multi-subject fMRI analysis confirm that the DHA method achieves superior performance to other state-of-the-art HA algorithms.