Variational Learning for Finite Inverted Dirichlet Mixture Models and Its Applications
Bibliographic record
Abstract
Clustering is an important step in data mining, machine learning, computer vision and image processing. It is the process of assigning similar objects to the same subset. Among available clustering techniques, finite mixture models have been remarkably used, since they have the ability to consider prior knowledge about the data. Employing mixture models requires, choosing a standard distribution, determining the number of mixture components and estimating the model parameters. Currently, the combination of Gaussian distribution, as the standard distribution, and Expectation Maximization (EM), as the parameter estimator, has been widely used with mixture models. However, each of these choices has its own limitations. In this thesis, these limitations are discussed and addressed via defining a variational inference framework for finite inverted Dirichlet mixture model, which is able to provide a better capability in modeling multivariate positive data, that appear frequently in many real world applications. Finite inverted Dirichlet mixtures enable us to model high-dimensional, both symmetric and asymmetric data. Compared to the conventional expectation maximization (EM) algorithm, the variational approach has the following advantages: it is computationally more efficient, it converges fast, and is able to estimate the parameters and the number of the mixture model components, automatically and simultaneously. The experimental results validate the presented approach on different synthetic datasets and shows its performance for two interesting and challenging real world applications, namely natural scene categorization and human activity classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".