Paper proposes a novel unsupervised feature selection method using K-means and ADMM.
problem Finding a subset of features for high-dimensional unsupervised learning problems.
method Developed K-means Derived Unsupervised Feature Selection (K-means UFS) using ADMM to solve NP-hard optimization.
result K-means UFS outperforms baselines in feature selection for clustering.
New method learns domain-invariant local feature patterns for unsupervised domain adaptation.
problem Performance degradation due to domain-shift in unsupervised domain adaptation.
method Jointly learns domain-invariant local feature patterns and holistic feature distributions.
result Superior performance on benchmark datasets compared to state-of-the-art methods.
Paper proposes BTuD for unsupervised feature selection.
problem Feature selection in unsupervised learning.
method Bayesian Tucker decomposition (BTuD) with Gaussian residual.
result Successfully applied to various datasets.
Study proposes using auxiliary classification to improve unsupervised anomaly detection.
problem Challenging anomaly detection in high-dimensional data.
method Use of an auxiliary classification task to extract features from unlabelled data by supervised learning.
result Our feature learning approach yields best anomaly detection performance.
New unsupervised feature selection method for imbalanced datasets.
problem Feature selection challenges in imbalanced multi-class datasets.
method Distance Rank Score using Spearman's Rank Correlation.
result Outperforms existing methods on clustering problems.
A novel unsupervised feature learning architecture using multi-clustering integration and MIRBM.
problem Feature learning without labeled data.
method Multi-clustering integration module with MIRBM, using K-means, affinity propagation, and spectral clustering.
result The proposed architecture outperforms state-of-the-art methods in clustering tasks.
Unsupervised segmentation learns features without labels, improving accuracy.
problem Discover and localize semantically meaningful categories in images without annotations.
method Separates feature learning from cluster compactification; distills unsupervised features into discrete semantic labels using a contrastive loss function.
result Significant improvement over prior state of the art on semantic segmentation challenges.
AEFS selects features from high-dimensional data using autoencoders.
problem Feature selection for high-dimensional data in computer vision and machine learning.
method Combines autoencoder regression and group lasso for unsupervised feature selection.
result AEFS selects more important features than traditional methods, including linear and nonlinear information.
A new method selects features for clustering using a block model.
problem Finding high-quality features for clustering in linked data.
method Building a block model on the graph and using it for feature selection.
result BMGUFS outperforms state-of-the-art methods in clustering performance.
Unsupervised clustering reveals novel TBI phenotypes.
problem Inadequate categorization of traumatic brain injury (TBI) based on symptoms.
method Applied unsupervised learning with GLRM feature selection.
result Identified four novel TBI phenotypes with distinct feature profiles.
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.
Unsupervised method selects genes for tumor subtype discovery.
problem High-dimensional tumor gene expression data with noisy variables and heterogeneity.
method Autoencoders for latent space learning, Multiple Kernel Learning for feature selection, clustering.
result Lower redundancy and better clustering performance compared to benchmarks.
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.
Improves unsupervised feature learning with an exclusivity concept.
problem Overfitting in AE-based unsupervised feature learning.
method Integrates exclusivity concept to enhance AE's latent feature representation.
result Significant improvement in performance compared to other methods.
A novel unsupervised feature selection method using subspace clustering and self-expressive model.
problem Feature selection for large datasets with minimal labeling effort.
method Subspace clustering with adaptive representation learning and regularized regression.
result The method effectively captures sample similarities and discriminative information.
Unsupervised learning finds features for better generalization in block stability prediction.
problem Improving generalization to unseen scenarios in block stability prediction.
method Training an unsupervised model to predict future frames of stable and unstable block configurations.
result Unsupervised model features support extrapolating stability prediction to unseen block configurations.
Paper proposes an unsupervised feature selection algorithm with stability guarantees.
problem Feature selection for dimension reduction and interpretability.
method Proposes a novel unsupervised feature selection algorithm with stability guarantees.
result The algorithm has superior generalization performance and stable selected features.
Unsupervised clustering can reproduce categorization systems if features and metrics are correctly selected.
problem Reproducing expert-provided categorization systems using unsupervised clustering.
method Investigated using toy datasets and real-world fund categorization. Used appropriate feature selection and a supervised Random Forest-based distance metric.
result Unsupervised clustering can reproduce ground truth classes if features and metrics are correctly selected.
A new method selects features for clustering without labels.
problem Identifying meaningful features in large datasets.
method Differentiable unsupervised feature selection using a gated Laplacian.
result The method improves clustering performance in noisy data.
New model selects uncorrelated and discriminative features for unsupervised feature selection.
problem Selecting uncorrelated and discriminative features in high-dimensional data.
method Adaptive graph-based generalized regression model with uncorrelated constraint and ℓ2,1-norm regularization. result The model effectively selects uncorrelated and discriminative features, improving clustering performance.
CoSCA improves unsupervised domain adaptation by better aligning ambiguous target samples.
problem Missing alignment of ambiguous target samples in unsupervised domain adaptation.
method CoSCA explicitly incorporates intra- and inter-class domain discrepancy, estimating label hypotheses and optimizing a contrastive loss with MMD for better global alignment.
result CoSCA outperforms state-of-the-art approaches in producing more discriminative features.
Paper uses motion cues to learn features for object detection.
problem Learning effective visual representations for object detection.
method Unsupervised motion-based segmentation of video frames to train a convolutional network.
result The learned representation significantly outperforms previous unsupervised approaches in object detection, especially in limited training data scenarios.
Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision. Using a large amount of unlabeled data, unsupervised feature learning methods are utilized to construct high-level representations that are discriminative en…
New measure based on space filling concept improves feature selection.
problem Improving feature selection in unsupervised learning.
method Adapted coverage measure for feature selection, implemented in a filter algorithm.
result Proposed filter algorithm selects informative variables robustly in high dimensions.
No supervision needed for deep networks with NAT framework.
problem Training deep networks without labeled data.
method Fix target representations (Noise As Targets) and constrain deep features to align with them.
result Deep features perform on par with state-of-the-art unsupervised methods.
Proposes an autoencoder-based unsupervised feature selection method.
problem Feature selection for high-dimensional data with non-linear structures.
method Autoencoder with spectral graph analysis for joint feature selection and manifold learning.
result Outperforms traditional methods in preserving both broad and local data structures.
The paper shows how integrating categorical semantics can enhance unsupervised domain translation.
problem Improving unsupervised domain translation between perceptually different domains.
method Learning invariant categorical semantic features in an unsupervised manner and conditioning them on the style encoder.
result Conditioning the style encoder on learned categorical semantics improves translation and stylization.
A novel method integrates feature and topology views for unsupervised graph representation learning.
problem Lack of mutual information across feature and topology views in graph representation learning.
method Proposes a multi-view representation learning module and a common representation learning module using mutual information maximization and reconstruction loss minimization.
result Demonstrates effectiveness in integrating feature and topology views, achieving comparable or better performance than supervised methods.
New method selects correlated features for unsupervised domain adaptation.
problem Improving unsupervised domain adaptation performance.
method Optimal transport theory for feature selection.
result Proposes a novel algorithm for feature selection.
MLS improves feature selection for imbalanced data.
problem Machine learning challenges with imbalanced high-dimensional data.
method Introduces Marginal Laplacian Score (MLS) for better feature selection.
result MLS improves performance on synthetic and public datasets.
Novel unsupervised feature selection method using multi-step Markov transition probability.
problem Neglected relationships between non-adjacent data points in feature selection.
method MMFS (Multi-step Markov transition probability for Feature Selection) approach, employing positive and negative viewpoints.
result MMFS effectively maintains data structure in unsupervised feature selection.
Paper presents a world model that learns invariant causal features using contrastive unsupervised learning.
problem Learning invariant causal features in unsupervised settings.
method Contrastive unsupervised learning with intervention invariant auxiliary task.
result Significantly outperforms state-of-the-art methods on out-of-distribution point navigation tasks.
Paper learns an explainer to interpret CNN features without annotations.
problem Interpreting complex features in CNNs without labeled data.
method Unsupervised learning of an explainer to decompose and reconstruct feature maps.
result Explainer learns to reconstruct CNN features without losing information.
Improved unsupervised anomaly detection using Random Forest.
problem Enhancing unsupervised anomaly detection accuracy.
method Training Random Forest to distinguish real and synthetic data, then applying transformed distances.
result Significant improvement in anomaly detection accuracy compared to other methods.
Approach for selecting features by discarding nuisance and correlated ones.
problem Large datasets with correlated and nuisance features.
method Laplacian score criterion, autoencoder architecture, concrete layer.
result Outperforms similar approaches in clustering performance.
Proposes a novel feature selection method for hypergraphs.
problem The 'curse of dimensionality' problem in feature selection.
method Unsupervised hypergraph feature selection via point-weighting and low-rank representation.
result Significant improvement over state-of-the-art feature selection methods.
CLIM-FS tackles mixed-missing multi-view unsupervised feature selection.
problem Mixed-missing multi-view data with incomplete features and views.
method Integrates imputation of missing views and variables into feature selection model based on nonnegative orthogonal matrix factorization.
result CLIM-FS outperforms state-of-the-art methods on real-world datasets.
New algorithms select and rank features from MTS without feature extraction.
problem Feature extraction step for MTS classification.
method Directly computes similarity between time series and assesses cluster structure matching labels.
result Techniques match labels well without feature extraction.
Proposes TFDF to learn transferable and discriminative features for unsupervised domain adaptation.
problem Difficult to induce supervised classifier without labeled data in unsupervised domain adaptation.
method TFDF optimizes transferability and discriminability by aligning distributions and minimizing class confusion.
result TFDF achieves better performance on real-world datasets compared to existing methods.
Improves information cascade models using contrastive training and DSTs.
problem Improving models of information cascades using limited labeled data.
method Proposes a contrastive training procedure for models of information cascades as directed spanning trees (DSTs).
result Unsupervised training with additional content features achieves significantly better results, reaching half the accuracy of a fully supervised model.
Unsupervised preprocessing can bias cross-validation estimates in regression models.
problem Bias in cross-validation estimates due to unsupervised preprocessing.
method Analysis of three preprocessing procedures: feature selection, grouping, and rescaling.
result Unsupervised preprocessing can introduce substantial bias into cross-validation estimates.
Algorithm identifies key features for efficient data reduction.
problem Efficiently identifying key features for large data sets.
method Stepwise regression algorithm for unsupervised feature selection.
result Efficient feature selection with reduced runtime complexity.
A new method identifies EMTE causes using CNN and unsupervised feature learning.
problem Identifying causes of EMTEs in power grids.
method Spatiotemporal unsupervised feature learning with CNN and softmax.
result Validation through EMTP and RTDS simulations of various events.
The paper investigates if graph embeddings capture key topological features.
problem Exploring if graph embeddings approximate traditional vertex level graph features.
method Predicting known topological features from graph embeddings using supervised and unsupervised methods.
result Several topological features are approximated by the embedding space, providing insight into how graph embeddings function.
Proposes a method to align and differentiate feature clusters for unsupervised domain adaptation.
problem Difficulty in obtaining labeled data for domain adaptation.
method Label propagation and cycle consistency to align feature clusters.
result Successfully formed aligned and discriminative clusters for better domain adaptation.
Unsupervised classifier performs as well as supervised ones on ImageNet dataset.
problem Achieving performance of supervised learning classifiers without labeled data.
method Incremental shift and rotation operations on selected hyperplanes.
result 6.2% Top 3 probability of error on ImageNet dataset.
Deep learning extracts brain MRI features without manual intervention.
problem Manual feature extraction from brain MRI data is time-consuming and error-prone.
method Large-scale unsupervised deep learning approach to learn generic feature representations.
result Low-dimensional representations of brain structure with comparable performance to FreeSurfer features.
Unsupervised learning for evolving data streams with STAM architecture.
problem Learning from non-stationary, unlabeled data streams over time.
method Self-Taught Associative Memory (STAM) architecture with online clustering, novelty detection, and feature storage.
result STAM architecture improves clustering and classification tasks compared to existing continual learning models.