Efficient Support Recovery of Mixtures of Sparse Linear Classifiers with Fewer Measurements
The support recovery problem in mixture of linear classifiers aims to identify the features relevant to the underlying decision rules when data is generated by a mixture of several linear decision rules. In particular, the goal is to recover the support (nonzero coordinates) of $l$ unknown $k$-sparse vectors from sign measurements. Each measurement is generated by selecting one of the $l$ vectors uniformly at random, and returning the sign of its inner product with a chosen measurement vector. In this paper, we propose adaptive and non-adaptive schemes that significantly improve upon prior results by simultaneously reducing the number of measurements and achieving sublinear decoding time....
| Authors | Xiaxin Li, Arya Mazumdar |
|---|---|
| Primary category | cs.LG |
| Published | 26 September 2026 |
| Last updated at source | 2 October 2026 |
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