Adaptive reduced rank regression
We study the low rank regression problem y = Mx + ε, where x and y are d1 and d2 dimensional vectors respectively. We consider the extreme high-dimensional setting where the number of observations n is less than d1 + d2. Existing algorithms are designed for settings where n is typically as large as...
| Main Authors: | , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Curran Associates
2021
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