Method calculates Shapley values for PCA reconstruction errors to explain anomaly detection.
problem Explaining PCA-based anomaly detection results.
method Utilizes probabilistic PCA view to compute Shapley values of reconstruction errors.
result Shapley values are more advantageous than raw errors for explaining anomalies.
iTimER learns from reconstruction errors to represent irregularly sampled time series.
problem Learning from irregularly sampled time series with missing data.
method iTimER models reconstruction errors as a proxy for unobserved values, using a mixup strategy and a Wasserstein metric.
result iTimER outperforms state-of-the-art methods in classification, interpolation, and forecasting tasks.
Probabilistic Autoencoder learns latent space weights' distribution.
problem Nonlinear model reconstruction error and sample quality.
method Normalizing flow for latent space weights' probability distribution.
result PAE achieves small reconstruction errors, high sample quality, and good performance.
New anomaly score for generative models without manifold assumptions.
problem Reconstruction error's theoretical limitations for generative models.
method Defining a new anomaly score compatible with generative models.
result The new score is theoretically sound and practical for auto-encoders.
RECol generates error columns to improve outlier detection.
problem Outlier detection in data with complex relationships.
method Generates reconstruction error columns for leave-one-out feature sets.
result Improves ROC-AUC and PR-AUC values of common outlier detection methods.
New algorithm learns stable LDSs with lower error and better control performance.
problem Learning stable LDSs from data with minimal reconstruction error and stability constraints.
method Proposes an optimization method using a recent characterization of stable matrices, iteratively improving reconstruction error and ensuring stability.
result Achieves orders-of-magnitude improvement in reconstruction error compared to existing methods.
Paper proposes RAN for better anomaly detection in time series data.
problem Anomaly detection algorithms often fail to accurately detect anomalies due to incomplete reconstruction of anomaly data.
method RAN uses adversarial learning and latent vector-constrained Autoencoder to ensure consistent reconstruction of anomaly data.
result RAN outperforms other algorithms in detecting meaningful anomalies with higher AUC-ROC scores.
This paper uses β-VAE for unsupervised anomaly detection in NSL-KDD.
problem Unsupervised anomaly detection in network traffic.
method β-VAE with latent space structure and reconstruction error.
result Latent space exploitation is more effective for classification tasks.
Paper explores CapsNet for anomaly detection in images.
problem Detecting anomalies in unseen images using CapsNet.
method Developed prediction-probability and reconstruction-error based normality scores.
result CapsNet-based methods outperform benchmarks in anomaly detection.
Two derivations of PCA for distributional data.
problem PCA for datasets of distributions.
method Two derivations: variance maximization and reconstruction error minimization.
result Closed-form solution for distributional PCA.
Adversarial autoencoders improve anomaly detection in images.
problem Anomaly detection in images is challenging when training data contains outliers.
method Adversarial autoencoders enforce a prior distribution on latent representations to identify and reject potential anomalies during training.
result Adversarial autoencoders significantly improve robustness to outliers during training.
Metalearning optimizes autoencoder dimensions for efficient data representation.
problem Selecting optimal dimension for autoencoder output to balance accuracy and complexity.
method Metalearning approach using actor-critic algorithm to dynamically adjust dimension.
result Automatic selection of minimum number of bases for optimal reconstruction.
We propose a fair principal component analysis method that balances reconstruction error and subgroup fairness.
problem Fairness and robustness in principal component analysis for consequential domains.
method Distributionally robust optimization over the Stiefel manifold with a Riemannian subgradient descent.
result The proposed method achieves better performance on real-world datasets compared to state-of-the-art baselines.
Study complex-valued VAEs for radar OOD detection.
problem Detecting out-of-distribution signals in complex radar environments.
method Proposed and compared several detection metrics for CVAE.
result CVAE-MSE and latent-based scores outperform ANMF-Tyler.
CoRAS adapts image acquisition rates for accurate reconstruction.
problem Determining when enough measurements are collected for accurate image reconstruction.
method Adaptive acquisition rate selection based on reconstruction error probability.
result CoRAS achieves target stopping-time coverage with fewer measurements.
DCAE learns compact latent representations for one-class novelty detection.
problem Learning compact latent representations for one-class novelty detection.
method DCAE learns compact and collapse-free latent representations through internal discriminative layers of GANs, reconstructing in-class data finely and exclusively.
result DCAE achieves state-of-the-art performance on novelty and adversarial example detection.
A new method for the unsupervised learning of sparse representations using autoencoders is proposed and implemented by ordering the output of the hidden units by their activation value and progressively reconstructing the input in this order. This can be done efficiently in parallel with the use of cumulative sums and …
It is well known that Principal Component Analysis (PCA) is strongly affected by outliers and a lot of effort has been put into robustification of PCA. In this paper we present a new algorithm for robust PCA minimizing the trimmed reconstruction error. By directly minimizing over the Stiefel manifold, we avoid deflatio…
Paper proposes a new method to learn similarity from data.
problem Learning similarity from data without losing manifold structure.
method Minimizing reconstruction error of kernel matrices.
result Significant improvements in clustering tasks compared to state-of-the-art methods.
New method identifies cause and effect using complexity of autoencoders.
problem Identifying cause and effect in complex systems.
method Adversarial training method to capture disentangled structure of causal models.
result Method identifies cause and effect based on complexity, not causality.
A new method for feature selection in high-dimensional data.
problem Dealing with noise and high-dimensional data in unsupervised feature selection.
method Sparse PCA via l2,p-norm regularization, combined with an efficient optimization algorithm. result The proposed method effectively selects features from real-world data sets.
Simplifies VAE for anomaly detection using rate-distortion theory.
problem Anomaly detection in unsupervised learning systems.
method Revisit VAE from information theory, incorporate model uncertainty.
result Competitive performance on benchmark datasets.
A novel feature selection method using a teacher-student network.
problem Feature selection for high-dimensional data in machine learning.
method Teacher-student network approach for feature selection.
result The proposed TSFS method outperforms existing methods in classification and clustering.
GCVAE improves disentanglement in VAEs while balancing reconstruction error.
problem Improving disentanglement in VAEs while maintaining low reconstruction error.
method Introduces three controllable Lagrangian hyperparameters to optimize reconstruction and KL divergence loss.
result GCVAE outperforms state-of-the-art models in disentanglement while balancing reconstruction.
Paper proposes efficient tensor completion method using Gaussian Process.
problem Tensor completion in high-dimensional data with unknown smooth functions.
method Gaussian Process Regression for initialization and TT-cross approximation for tensor rank selection.
result Improved reconstruction error compared to random initialization.
This paper presents a method to summarize directed graphs while preserving edge information.
problem Summarizing directed graphs while maintaining edge directionality.
method A model based on minimizing reconstruction error with non-negative constraints, related to Max-Cut criterion, using multiplicative update algorithms.
result The proposed method identifies compressed nodes and directed compressed relations, providing a more accurate representation of directed graphs.
Improves model-based control and exploration by estimating model uncertainty.
problem Inaccuracies in model predictions lead to frequent re-planning, inefficiency, and unreliability.
method Estimates model uncertainty using reconstruction error and uses it for better control and active exploration.
result Improves control performance and exploration efficiency by choosing confident model predictions and planning for high uncertainty.
EVODiff optimizes DM inference by reducing conditional entropy, improving image generation.
problem Slow and inaccurate inference in diffusion models.
method Entropy-aware variance optimization for efficient inference.
result Significant improvement in image generation quality and efficiency.
New PCA method detects faults using occupation kernels.
problem Fault detection in dynamical systems.
method Occupation kernel PCA for irregularly sampled data.
result Validation of reconstruction error approach for fault detection.
Study reconstructs hidden perfect matchings in random graphs with specific edge weights.
problem Reconstructing hidden perfect matchings in random weighted bipartite graphs.
method Analyzes the maximum likelihood estimator for matching reconstruction under different probability distributions of edge weights.
result Sharp threshold and infinite-order phase transition in reconstruction error for different probability distributions.
In this on-going work, I explore certain theoretical and empirical implications of data transformations under the PCA. In particular, I state and prove three theorems about PCA, which I paraphrase as follows: 1). PCA without discarding eigenvector rows is injective, but looses this injectivity when eigenvector rows are…
Paper presents J-RFDL for robust DL in compressed space, improving data representation robustness and accuracy.
problem Improving data representation robustness and accuracy in the presence of noise and outliers.
method Joint Robust Factorization and Projective Dictionary Learning (J-RFDL) in a factorized compressed space.
result Delivers superior performance in data representation and classification over state-of-the-art methods.
DRIO improves time series imputation by minimizing reconstruction error and distributional divergence.
problem Bias in imputation due to mismatch between observed and true data distributions.
method DRIO minimizes reconstruction error and worst-case divergence using Wasserstein ambiguity set.
result DRIO consistently provides robust imputation and improved forecasting.
A fall is an abnormal activity that occurs rarely, so it is hard to collect real data for falls. It is, therefore, difficult to use supervised learning methods to automatically detect falls. Another challenge in using machine learning methods to automatically detect falls is the choice of engineered features. In this p…
TadGAN detects anomalies in time series data using GANs and LSTM.
problem Challenges in detecting anomalies in time series data, especially without labeled data.
method TadGAN uses Generative Adversarial Networks (GANs) with LSTM Recurrent Neural Networks to capture temporal correlations and compute anomaly scores.
result TadGAN outperforms 8 baseline methods in most cases, achieving the highest averaged F1 score.
POTATOES improves autoencoder UOD accuracy without tuning.
problem Improving unsupervised outlier detection accuracy.
method Randomly partition data, overfit each part with an autoencoder, use max reconstruction error as anomaly score.
result Significant improvement in UOD performance for dense inlier sets.
Paper proposes a method to break symmetries in Bayesian matrix factorization.
problem Symmetries in posterior distribution reduce MCMC sampling efficiency.
method Modification to Gaussian prior mean and covariance to break symmetries.
result Breaking symmetries leads to lower autocorrelation and reconstruction errors.
Simpler autoencoder with regularization outperforms complex alternatives.
problem Autoencoders can generalize to anomalous data, leading to low reconstruction errors.
method Targeted collapse regularization to improve autoencoder performance.
result The method matches and often outperforms more complex anomaly detection techniques.
RFA-LCF improves clustering accuracy by robustly handling noise and errors.
problem Inaccurate representation and clustering results due to noise and hard constraints.
method Integrates robust flexible CF, sparse local-coordinate coding, and adaptive weighting into a unified model.
result Delivers state-of-the-art clustering results on public databases.
This paper aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretical summarize the general properties of all algorithms that are based on traditional Auto-Encoders: 1) The reconstruction error of the input can not be lower than a lower bound, wh…
ALAD uses GANs to detect anomalies in complex data.
problem Effective anomaly detection for complex, high-dimensional data.
method Adversarially learned features derived from bi-directional GANs.
result Significantly improved anomaly detection performance.
We propose rectified factor networks (RFNs) to efficiently construct very sparse, non-linear, high-dimensional representations of the input. RFN models identify rare and small events in the input, have a low interference between code units, have a small reconstruction error, and explain the data covariance structure. R…
New method detects anomalies without bias, improving on autoencoder reconstruction errors.
problem Inherent biases in autoencoder-based anomaly detection methods.
method Introduces a Lipschitz anomaly discriminator trained to detect differences between training data and corruptions.
result Successfully detects anomalies with guarantees on certain Wasserstein distances.
Robust SPA improves NMF robustness to outliers.
problem Non-robustness to outliers in SPA.
method Integrates outlier robustness and data fitting into SPA.
result RSPA is robust to outliers and maintains low-noise robustness.
This paper tackles fairness in PCA by balancing it with reconstruction error.
problem Fairness concerns in PCA due to different group representation errors.
method A multi-objective optimization approach to balance fairness and reconstruction error.
result Achieving fairness with minimal loss in reconstruction error.
Binary autoencoder with sparse hidden layer preserves information and zero reconstruction error.
problem Preserving information and zero reconstruction error in binary neural networks.
method Binary autoencoder with random binary weights, sparse hidden layer, and varying neuron thresholds.
result Zero reconstruction error for any input with a large hidden layer and varying neuron thresholds.
Capsule networks detect and diagnose adversarial images better than CNNs.
problem Detecting and diagnosing adversarial images in neural networks.
method Class-conditional capsule reconstruction and reconstructive attack.
result Capsule networks outperform CNNs in detecting and diagnosing adversarial images.
Using methods of statistical physics, we analyse the error of learning couplings in large Ising models from independent data (the inverse Ising problem). We concentrate on learning based on local cost functions, such as the pseudo-likelihood method for which the couplings are inferred independently for each spin. Assum…