Unsupervised Anomaly Detection using Deep Autoencoding Mixture of Probabilistic Principal Component Analyzers
Bibliographic record
Abstract
Although many unsupervised anomaly detection algorithms with outstanding performance have been proposed in last few decades, their performance for high-dimensional data is not guaranteed. Therefore, this paper proposes a new framework called deep autoencoding mixture of probabilistic principal component analyzers (DA-MPPCA). This framework uses deep autoencoder (DAE) as a compression network to prepare the low-dimensional representations for a subsequent estimation network NN-MPPCA. Different to conventional MPPCA that is trained by EM algorithm, NN-MPPCA is the neural network form of MPPCA which parameters can be updated via back-propagation algorithm. By jointly training DAE and NN-MPPCA in an end-to-end manner using a defined loss function, we force both dimensionality reduction and density estimation tasks into the unified framework. The experimental results on a variety of public datasets have demonstrated the superior performance of DA-MPPCA over both shallow and deep baseline models with an improvement on F1-score up to 7% over the best baseline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".