Spectral Learning of Mixture of Hidden Markov Models
Authors: Cem Subakan, Johannes Traa, Paris Smaragdis
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the validity of our approach on synthetic and real data. 4 Experiments |
| Researcher Affiliation | Collaboration | Department of Computer Science, University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign Adobe Systems, Inc. |
| Pseudocode | Yes | Algorithm 1 Spectral Learning for Mixture of Hidden Markov Models |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We ran an experiment on the handwritten character trajectory dataset from the UCI machine learning repository [8]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. It mentions using 'synthetic data' and the 'UCI machine learning repository' but does not detail train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We used the parameters K = 3, M = 4, L = 20, and we have 2 sequences with length T for each cluster. We used a Gaussian observation model with unit observation variance and the columns of the emission matrices O1:K were drawn from zero mean spherical Gaussian with variance 2. We set L = 20, K = 5, M = 3. We used uniform mixing proportions and uniform initial state distribution. We used four restarts for EM. |