# Collaborative Regression

arXiv: https://arxiv.org/abs/1401.5823 · HTML: https://mlsanity.com/paper/1401.5823/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2014.

## Built on by (most cited first, 9 of 9)

- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction](https://mlsanity.com/paper/2312.06704/) (2023)
- [SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling](https://mlsanity.com/paper/2303.17368/) (2023)
- [Egocentric Whole-Body Motion Capture with FisheyeViT and Diffusion-Based Motion Refinement](https://mlsanity.com/paper/2311.16495/) (2023)
- [GUAVA: Generalizable Upper Body 3D Gaussian Avatar](https://mlsanity.com/paper/2505.03351/) (2025)
- [Cooperative learning for multiview analysis](https://mlsanity.com/paper/2112.12337/) (2021)
- [sJIVE: Supervised Joint and Individual Variation Explained](https://mlsanity.com/paper/2102.13278/) (2021)
- [ProxyCap: Real-time Monocular Full-body Capture in World Space via Human-Centric Proxy-to-Motion Learning](https://mlsanity.com/paper/2307.01200/) (2023)
- [Multi-modality fusion using canonical correlation analysis methods: Application in breast cancer survival prediction from histology and genomics](https://mlsanity.com/paper/2111.13987/) (2021)

## Abstract (arXiv)

We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with this type of data is ``sparse multiple canonical correlation analysis'' (sparse mCCA). All of the current sparse mCCA techniques are biconvex and thus have no guarantees about reaching a global optimum. We propose a method for performing sparse supervised canonical correlation analysis (sparse sCCA), a specific case of sparse mCCA when one of the datasets is a vector. Our proposal for sparse sCCA is convex and thus does not face the same difficulties as the other methods. We derive efficient algorithms for this problem, and illustrate their use on simulated and real data.