Modified neural network enhances unsupervised anomaly detection.
problem Unsupervised anomaly detection in multimodal data.
method Neural network with modified random projection outlyingness.
result Performance comparable to state-of-the-art methods.
PCA outperforms random projections in retaining second order signals from latent groups.
problem Preserving second order structure in latent groups under unsupervised linear projections.
method Theoretical framework and quasi-exhaustive enumeration of projections.
result PCA outperforms random projections in retaining second order signals across a broad range of data-generating parameters.
Improves deep learning with less labeled data using unsupervised projection.
problem Lack of labeled data for deep learning models.
method Modified unsupervised discriminant projection as a regularization term for semi-supervised learning.
result Proposes an algorithm that enhances classification performance with minimal labeled data.
Proposes manifold-based unsupervised anomaly detection for visual data.
problem Rare anomalies in unlabeled data.
method Constant curvature manifolds, hyperspherical Variational Auto-Encoders (VAE) with gyroplane layer.
result State-of-the-art results on visual anomaly benchmarks and histopathology.
SUOD accelerates unsupervised outlier detection for large datasets.
problem Scalability issues in unsupervised outlier detection for high-dimensional datasets.
method Three-module acceleration framework: Random Projection, Balanced Parallel Scheduling, Pseudo-supervised Approximation.
result SUOD significantly improves efficiency and scalability in outlier detection.
New algorithm improves sparse-view tomography without needing ground-truth data.
problem Poor image reconstructions with sparse projections and non-uniform sensors.
method Unsupervised deep learning with CNN and STN modules.
result Significantly outperforms filtered backprojection in sparse-view scenarios.
This work proposes unsupervised learning by predicting random distances in neural networks.
problem Lack of labelled data in unsupervised learning tasks.
method Train neural networks to predict random distances in a randomly projected space, optimizing for genuine class structures.
result Learned representations outperform state-of-the-art methods in anomaly detection and clustering.
Proposes a fuzzy rule-based method for data visualization.
problem Preserving neighborhood relationships and handling non-linear manifolds in data visualization.
method Uses a first-order Takagi-Sugeno model with clusters and Geodesic c-means clustering for rule generation and parameter estimation.
result Behaves desirably and performs better than or comparable to other methods.
Unsupervised learning classifies transient noise in gravitational wave detectors.
problem Transient noise interferes with gravitational wave signals, causing instability.
method Combines variational autoencoder and invariant information clustering.
result Consistent classification with Gravity Spy project labels.
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.
This is a detailed tutorial paper which explains the Principal Component Analysis (PCA), Supervised PCA (SPCA), kernel PCA, and kernel SPCA. We start with projection, PCA with eigen-decomposition, PCA with one and multiple projection directions, properties of the projection matrix, reconstruction error minimization, an…
Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex. W…
RAEUFS selects features from data without labels, improving robustness to outliers.
problem Feature selection in high-dimensional data, especially in the presence of outliers.
method RAEUFS uses a deep autoencoder to learn nonlinear feature representations, improving robustness to outliers.
result RAEUFS outperforms state-of-the-art UFS methods in both clean and outlier-contaminated data settings.
SPACE models complex scenes by decomposing objects and backgrounds.
problem Scalability and unsupervised object-oriented scene representation learning.
method Generative latent variable model combining spatial-attention and scene-mixture approaches.
result SPACE achieves factorized object representations and decomposes complex scenes.
Generative Adversarial networks (GANs) have obtained remarkable success in many unsupervised learning tasks and unarguably, clustering is an important unsupervised learning problem. While one can potentially exploit the latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not re…
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.
Paper reproduces and enhances a method for cross-lingual word embeddings.
problem Creating robust cross-lingual mappings of word embeddings without supervision.
method Reproduces and enhances a self-learning method with grid search for hyperparameters.
result Model's robustness is demonstrated across four new languages.
In the last decade, supervised deep learning approaches have been extensively employed in visual odometry (VO) applications, which is not feasible in environments where labelled data is not abundant. On the other hand, unsupervised deep learning approaches for localization and mapping in unknown environments from unlab…
Optimizes t-SNE for high-dimensional data with random projections.
problem High computational cost of t-SNE for high-dimensional data.
method Use random projections to reduce high-dimensional data to a few dimensions, then apply t-SNE.
result Random projections preserve clustering while significantly reducing t-SNE runtime.
In this paper we propose a strategy for semi-supervised image classification that leverages unsupervised representation learning and co-training. The strategy, that is called CURL from Co-trained Unsupervised Representation Learning, iteratively builds two classifiers on two different views of the data. The two views c…
PhyloVAE learns tree topologies without supervision.
problem Learning accurate tree representations from evolutionary data.
method Unsupervised learning via variational autoencoders with efficient tree generation.
result PhyloVAE generates high-resolution tree topologies efficiently.
Projective simulation converges to optimal behavior in Markov decision processes.
problem Optimizing reinforcement learning in Markov decision processes.
method Projective simulation framework applied to reinforcement learning.
result Projective simulation converges to optimal behavior in a large class of Markov decision processes.
Paper explores how unsupervised learning reduces financial crime risks.
problem Identifying high-risk financial groups from complex data.
method Combines clustering and dimensionality reduction techniques.
result KPCA outperforms other techniques in reducing financial crime risks.
Novel unsupervised audio source separation using generative priors.
problem Challenges of supervised methods in audio source separation.
method Generative priors trained on individual sources using projected gradient descent optimization.
result Effective recovery of constituent sources with good-quality estimates.
Deep metric learning detects anomalies without labels.
problem Unsupervised anomaly detection for high-dimensional data.
method Deep metric learning with end-to-end optimization, data distillation, hard mining.
result Significant performance gains over state-of-the-art methods.
Inspired by the advances in biological science, the study of sparse binary projection models has attracted considerable recent research attention. The models project dense input samples into a higher-dimensional space and output sparse binary data representations after the Winner-Take-All competition, subject to the co…
This work predicts and interpolates long-range videos using unsupervised landmarks.
problem Predicting and interpolating long-range video data with occlusions and appearance changes.
method Unsupervised latent structure inference followed by temporal prediction in a latent space.
result High-quality long-range video interpolation and extrapolation achieved through landmark representation.
Modern techniques simplify complex high-dimensional data.
problem Complex, high-dimensional data.
method Unsupervised dimension reduction techniques.
result Simplified representation of high-dimensional data.
Unsupervised two-view learning, or detection of dependencies between two paired data sets, is typically done by some variant of canonical correlation analysis (CCA). CCA searches for a linear projection for each view, such that the correlations between the projections are maximized. The solution is invariant to any lin…
This paper deals with unsupervised clustering with feature selection. The problem is to estimate both labels and a sparse projection matrix of weights. To address this combinatorial non-convex problem maintaining a strict control on the sparsity of the matrix of weights, we propose an alternating minimization of the Fr…
New method improves unsupervised feature learning for natural data.
problem Natural data's correlated and long-tail distribution challenges instance-level contrastive learning.
method Cross-level instance-group discrimination (CLD) to integrate between-instance similarity.
result CLD achieves new state-of-the-art performance on various datasets.
Unified framework for DR and clustering using Gromov-Wasserstein.
problem Capturing structure in high-dimensional datasets.
method Distributional reduction framework using Gromov-Wasserstein.
result Unified approach recovers DR and clustering as special cases.
New method sets explicit sparsity for groups of vectors in deep learning and NMF.
problem Tackles the challenge of achieving a desired average sparsity level in vector groups.
method Designs a new sparse projection method that sets the sparsity level for the whole set explicitly and automatically tunes the sparsity of each vector.
result Shows significant improvements in accuracy and reconstruction errors compared to existing methods in deep learning and NMF.
Sparse Subspace Clustering (SSC) is a popular unsupervised machine learning method for clustering data lying close to an unknown union of low-dimensional linear subspaces; a problem with numerous applications in pattern recognition and computer vision. Even though the behavior of SSC for complete data is by now well-un…
Principal component analysis (PCA) is an unsupervised method for learning low-dimensional features with orthogonal projections. Multilinear PCA methods extend PCA to deal with multidimensional data (tensors) directly via tensor-to-tensor projection or tensor-to-vector projection (TVP). However, under the TVP setting, i…
Multi-task learning shares information between related tasks, sometimes reducing the number of parameters required. State-of-the-art results across multiple natural language understanding tasks in the GLUE benchmark have previously used transfer from a single large task: unsupervised pre-training with BERT, where a sep…
Paper extends multivariate rank tests for robust subspace detection.
problem Testing distributional similarity in multivariate data.
method Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport.
result Trade-off between detection power and false alarm rate via projections.
Encoder-decoder recurrent neural network models (RNN Seq2Seq) have achieved great success in ubiquitous areas of computation and applications. It was shown to be successful in modeling data with both temporal and spatial dependencies for translation or prediction tasks. In this study, we propose an embedding approach t…
New algorithm improves signal recovery from noisy measurements with theoretical guarantees.
problem Recovering signals from noisy measurements in inverse problems.
method Wasserstein-based projections (WP) replacing analytic regularization with data-driven denoising.
result WP approximates true projection with high probability, providing theoretical guarantees.
The study examines the distribution of projections of Gaussian data points and its implications for learning models.
problem Understanding the distribution of projections of Gaussian data points in high dimensions.
method Analyzes the asymptotic behavior of projections of i.i.d. standard Gaussian vectors in Rd onto m-dimensional subspaces. result Establishes bounds on the Wasserstein radius of the set of probability distributions arising from these projections.
Novel unsupervised MIG detectors improve signal detection in cluttered environments.
problem Signal detection in nonhomogeneous clutter environments.
method Developed novel discriminative MIG detectors using HPD matrices and geometric measures.
result Improved signal detection performance compared to conventional methods.
Compressed sensing techniques enable efficient acquisition and recovery of sparse, high-dimensional data signals via low-dimensional projections. In this work, we propose Uncertainty Autoencoders, a learning framework for unsupervised representation learning inspired by compressed sensing. We treat the low-dimensional …
Paper proposes a new method for joint feature selection and graph learning.
problem Previous methods suffer from neglecting joint formulation and lack of graph learning.
method Formulates multi-view feature selection with orthogonal decomposition, incorporates cross-space locality preservation, and uses a unified objective function for simultaneous learning.
result Demonstrates superior performance in multi-view feature selection and graph learning tasks.
This work bridges competitive learning with gradient-based learning for faster feature extraction.
problem Lack of powerful feature extractors in competitive learning methods.
method Introduces gradient-based competitive layers for feature extraction.
result Demonstrates theoretical equivalence and faster convergence of gradient-based competitive layers.
OracleAD detects multivariate time series anomalies without labels.
problem Rare and unlabeled multivariate time series anomalies.
method OracleAD encodes past sequences into causal embeddings, projects them into a latent space, and identifies anomalies based on deviations from a stable latent structure.
result OracleAD achieves state-of-the-art results and is interpretable.
Paper tackles unsupervised learning of 3D shapes from single images.
problem Learning 3D shapes from single images without supervision.
method Generative models, variational auto-encoders, adversarial methods.
result Model learns 3D shapes and poses from single images, showing potential for various datasets.
In this paper, we train a recurrent neural network to learn dynamics of a chaotic road environment and to project the future of the environment on an image. Future projection can be used to anticipate an unseen environment for example, in autonomous driving. Road environment is highly dynamic and complex due to the int…
In representation learning and non-linear dimension reduction, there is a huge interest to learn the 'disentangled' latent variables, where each sub-coordinate almost uniquely controls a facet of the observed data. While many regularization approaches have been proposed on variational autoencoders, heuristic tuning is …