A Spectral Algorithm for Inference in Hidden semi-Markov Models
Authors: Igor Melnyk, Arindam Banerjee
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on synthetic and real data demonstrate the advantage of the algorithm over EM in terms of speed and accuracy, especially for large data sets. Keywords: Graphical models, hidden semi-Markov model, spectral algorithm, tensor analysis, aviation safety |
| Researcher Affiliation | Collaboration | Igor Melnyk EMAIL IBM T. J. Watson Research Center Yorktown Heights, NY 10598, USA Arindam Banerjee EMAIL Department of Computer Science and Engineering University of Minnesota Minneapolis, MN 55414, USA |
| Pseudocode | Yes | Algorithm 1 Basic Spectral Algorithm for HSMM inference Input: Training sequences: Si = {oi 1, . . . , oi Ti}, i = 1, . . . , N. Testing sequence: Stest = {otest 1 , . . . , otest T }. Output: p(Stest) |
| Open Source Code | No | The paper does not provide any explicit statements about code availability, nor does it include links to a code repository. Mentions of algorithms like EM are references to existing methods, not the authors' implementation code. |
| Open Datasets | Yes | NASA. Flight data set. Available at https://c3.nasa.gov/dashlink/projects/85/. |
| Dataset Splits | Yes | For this, we defined two HSMMs, one with no = 3, nx = 2, nd = 2 and another with no = 5, nx = 4, nd = 6. For each model, we generated a set of Ntrain = {500, 1000, 5000, 104, 105} training and Ntest ==1000 testing sequences, each of length T = 100. |
| Hardware Specification | No | The paper thanks the Minnesota Supercomputing Institute (MSI) for computing support, but does not specify any particular hardware components such as CPU or GPU models, or memory. |
| Software Dependencies | No | The paper mentions algorithms like 'expectation maximization (EM)' and 'Baum-Welch algorithm' but does not specify any software libraries or packages with version numbers used for implementation. |
| Experiment Setup | Yes | For this, we defined two HSMMs, one with no = 3, nx = 2, nd = 2 and another with no = 5, nx = 4, nd = 6. For each model, we generated a set of Ntrain = {500, 1000, 5000, 104, 105} training and Ntest ==1000 testing sequences, each of length T = 100. |