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Latent Tree Approximation in Linear Model
October 05, 2017 Β· Declared Dead Β· π IEEE International Conference on Acoustics, Speech, and Signal Processing
"No code URL or promise found in abstract"
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Authors
Navid Tafaghodi Khajavi
arXiv ID
1710.01838
Category
cs.IT: Information Theory
Citations
4
Venue
IEEE International Conference on Acoustics, Speech, and Signal Processing
Last Checked
4 months ago
Abstract
We consider the problem of learning underlying tree structure from noisy, mixed data obtained from a linear model. To achieve this, we use the expectation maximization algorithm combined with Chow-Liu minimum spanning tree algorithm. This algorithm is sub-optimal, but has low complexity and is applicable to model selection problems through any linear model.
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