# Subspace Learning with Partial Information

arXiv: https://arxiv.org/abs/1402.4844 · HTML: https://mlsanity.com/paper/1402.4844/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2014.

## Built on by (most cited first, 3 of 3)

- [One-sided Matrix Completion from Two Observations Per Row](https://mlsanity.com/paper/2306.04049/) (2023)
- [Bit Efficient Toeplitz Covariance Estimation](https://mlsanity.com/paper/2412.12678/) (2024)
- [Compressive Toeplitz Covariance Estimation From Few-Bit Quantized Measurements With Applications to DOA Estimation](https://mlsanity.com/paper/2512.22527/) (2025)

## Abstract (arXiv)

The goal of subspace learning is to find a k-dimensional subspace of ℝd, such that the expected squared distance between instance vectors and the subspace is as small as possible. In this paper we study subspace learning in a partial information setting, in which the learner can only observe r ≤ d attributes from each instance vector. We propose several efficient algorithms for this task, and analyze their sample complexity