Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine

• URL: http://arxiv.org/abs/2403.12672v1
• Date: Tue, 19 Mar 2024 12:13:52 GMT
• Title: Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine
• Authors: Kaiji Sekimoto, Muneki Yasuda,
• Abstract summary: In GBRBM-based anomaly detection, normal and anomalous data are classified based on a score that is identical to an energy function of the marginal GBRBM. We propose a measure that improves score's interpretability based on its cumulative distribution. We also establish a guideline for setting the threshold using the interpretable measure.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gaussian-Bernoulli restricted Boltzmann machines (GBRBMs) are often used for semi-supervised anomaly detection, where they are trained using only normal data points. In GBRBM-based anomaly detection, normal and anomalous data are classified based on a score that is identical to an energy function of the marginal GBRBM. However, the classification threshold is difficult to set to an appropriate value, as this score cannot be interpreted. In this study, we propose a measure that improves score's interpretability based on its cumulative distribution, and establish a guideline for setting the threshold using the interpretable measure. The results of numerical experiments show that the guideline is reasonable when setting the threshold solely using normal data points. Moreover, because identifying the measure involves computationally infeasible evaluation of the minimum score value, we also propose an evaluation method for the minimum score based on simulated annealing, which is widely used for optimization problems. The proposed evaluation method was also validated using numerical experiments.

Related papers

• Language Generation with Strictly Proper Scoring Rules [70.340673452404]
  We propose a strategy for adapting scoring rules to language generation, allowing for language modeling with any non-local scoring rules. We train language generation models using two classic strictly proper scoring rules, the Brier score and the Spherical score, as alternatives to the logarithmic score.
  arXiv  Detail & Related papers  (2024-05-29T09:09:00Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• Denoising Likelihood Score Matching for Conditional Score-based Data Generation [22.751924447125955]
  We propose a novel training objective called Denoising Likelihood Score Matching (DLSM) loss to match the gradients of the true log likelihood density. Our experimental evidence shows that the proposed method outperforms the previous methods noticeably in terms of several key evaluation metrics.
  arXiv  Detail & Related papers  (2022-03-27T04:37:54Z)
• Learning to Estimate Without Bias [57.82628598276623]
  Gauss theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non linear settings via deep learning with bias constraints. A second motivation to BCE is in applications where multiple estimates of the same unknown are averaged for improved performance.
  arXiv  Detail & Related papers  (2021-10-24T10:23:51Z)
• Near-optimal inference in adaptive linear regression [60.08422051718195]
  Even simple methods like least squares can exhibit non-normal behavior when data is collected in an adaptive manner. We propose a family of online debiasing estimators to correct these distributional anomalies in at least squares estimation. We demonstrate the usefulness of our theory via applications to multi-armed bandit, autoregressive time series estimation, and active learning with exploration.
  arXiv  Detail & Related papers  (2021-07-05T21:05:11Z)
• Evaluating State-of-the-Art Classification Models Against Bayes Optimality [106.50867011164584]
  We show that we can compute the exact Bayes error of generative models learned using normalizing flows. We use our approach to conduct a thorough investigation of state-of-the-art classification models.
  arXiv  Detail & Related papers  (2021-06-07T06:21:20Z)
• Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning [78.83598532168256]
  Marginal-likelihood based model-selection is rarely used in deep learning due to estimation difficulties. Our work shows that marginal likelihoods can improve generalization and be useful when validation data is unavailable.
  arXiv  Detail & Related papers  (2021-04-11T09:50:24Z)
• Detecting Label Noise via Leave-One-Out Cross Validation [0.0]
  We present a simple algorithm for identifying and correcting real-valued noisy labels from a mixture of clean and corrupted samples. A heteroscedastic noise model is employed, in which additive Gaussian noise terms with independent variances are associated with each and all of the observed labels. We show that the presented method can pinpoint corrupted samples and lead to better regression models when trained on synthetic and real-world scientific data sets.
  arXiv  Detail & Related papers  (2021-03-21T10:02:50Z)
• Bias-Corrected Peaks-Over-Threshold Estimation of the CVaR [2.552459629685159]
  Conditional value-at-risk (CVaR) is a useful risk measure in fields such as machine learning, finance, insurance, energy, etc. When measuring very extreme risk, the commonly used CVaR estimation method of sample averaging does not work well. To mitigate this problem, the CVaR can be estimated by extrapolating above a lower threshold than the VaR.
  arXiv  Detail & Related papers  (2021-03-08T20:29:06Z)
• Deterministic Gaussian Averaged Neural Networks [7.51557557629519]
  We present a deterministic method to compute the Gaussian average of neural networks used in regression and classification. We use this equivalence to certify models which perform well on clean data but are not robust to adversarial perturbations.
  arXiv  Detail & Related papers  (2020-06-10T20:53:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.