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On The relationship between State-Space-Subspace-Based and Maximum-Likelihood System Identification methods.
| Content Provider | CiteSeerX |
|---|---|
| Author | Ninness, Brett Gibson, Stuart |
| Abstract | State-Space Subspace Identification methods obtain system estimates in closed form, and this is in contrast to Maximum Likelihood methods which, although provably consistent and statistically efficient, require an iterative approach to solve an optimisation problem (which is possibly non-convex) over the likelihood surface. Particularly in signal processing and pattern recognition, the so-called Expectation Maximisation (EM) method is a popular way of performing these latter iterations. This paper establishes that a subspace identification method can, in fact, be viewed as one iteration of the EM algorithm. As such, a link between subspace and Maximum Likelihood methods is established. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Maximum-likelihood System Identification Method Maximum Likelihood Method Signal Processing State-space Subspace Identification Method Iterative Approach Subspace Identification Method Pattern Recognition Popular Way Closed Form Likelihood Surface Em Algorithm Optimisation Problem Latter Iteration System Estimate So-called Expectation Maximisation |
| Content Type | Text |
| Resource Type | Article |
