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.
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.
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.
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.
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM). In the multi-clustering integration module, we apply three unsupervised K-means, affinity propagation and spectral c…
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.
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.
Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation methods focus on holistic feature alignment by matching source and target holistic feature distributions, without considering local feature…
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.
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…
High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty. Feature selection which selects a subset from observed features is a widely used approach for improving performance and effectiveness of machine learning models with high-dimensional…
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 novel framework for unsupervised domain adaptation using causal representations.
problem Transferability of deep model representations across domains is limited.
method Integrates causal inference into deep learning pipeline for domain-invariant feature learning.
result Demonstrates superior performance in unsupervised domain adaptation using causal representations.
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.
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.
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.
Proposes a few-shot learning method for feature selection without labeled data.
problem Feature selection in unlabeled data with limited instances.
method Uses Concrete random variables and permutation-invariant neural networks to select features from multiple source tasks.
result Outperforms existing methods in feature selection performance.
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…
Humans learn a predictive model of the world and use this model to reason about future events and the consequences of actions. In contrast to most machine predictors, we exhibit an impressive ability to generalize to unseen scenarios and reason intelligently in these settings. One important aspect of this ability is ph…
Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications. However, training these networks requires significant amounts of supervision. This paper introduces a generic framework to train deep networks, end-to-end, with no supervision. We propose to fix a se…
Improves unsupervised domain adaptation by mixing source and target domains.
problem Improves unsupervised domain adaptation by mixing source and target domains.
method Enforces training constraints across domains using mixup formulation and feature-level consistency regularizer.
result Significantly improves state-of-the-art performance on image classification and human activity recognition tasks.
This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segme…
GANs can be used to extract Fisher vectors for unsupervised feature learning.
problem Unsupervised feature extraction for classification and similarity tasks.
method Derive Fisher Information and Fisher Vectors from GANs' density model.
result GAN-induced Fisher Vectors perform competitively in unsupervised feature extraction.
Unsupervised learning is becoming more and more important recently. As one of its key components, the autoencoder (AE) aims to learn a latent feature representation of data which is more robust and discriminative. However, most AE based methods only focus on the reconstruction within the encoder-decoder phase, which ig…
We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithm…
We describe the 1st place winning approach for the CIKM Cup 2016 Challenge. In this paper, we provide an approach to reasonably identify same users across multiple devices based on browsing logs. Our approach regards a candidate ranking problem as pairwise classification and utilizes an unsupervised neural feature ense…
An unsupervised learning classification model is described. It achieves classification error probability competitive with that of popular supervised learning classifiers such as SVM or kNN. The model is based on the incremental execution of small step shift and rotation operations upon selected discriminative hyperplan…
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.
New unsupervised learning technique learns independent kernels for better machine learning tasks.
problem Improving unsupervised representation learning for machine learning tasks.
method Stacking convolutional transforms using alternating proximal minimization scheme.
result DCTL outperforms shallow version CTL on benchmark datasets.
Minimalistic unsupervised learning with sparse manifold transform achieves SOTA performance.
problem Achieving state-of-the-art unsupervised learning performance without complex engineering.
method Sparse manifold transform, leveraging sparse coding, manifold learning, and slow feature analysis.
result 99.3% KNN top-1 accuracy on MNIST, 81.1% on CIFAR-10, and 53.2% on CIFAR-100.
The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier. Existing methods typically focus on vectorized data and offer little support for relational data, which additionally describe relationships among instances. In this work we…
This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., the explainer uses interpretable visual concepts to explain features in middle conv-layers of a CNN. Given feature maps of a conv-layer of the CNN, the explaine…
Unsupervised methods detect faults in industrial systems with limited data.
problem Challenges in training data-driven fault detection models with scarce or unavailable data.
method Proposes five approaches: baseline, incremental learning, fleet comparison, and UFAN.
result UFAN outperforms other methods in detecting faults in dissimilar units.
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.
Nonlinear independent component analysis (ICA) provides an appealing framework for unsupervised feature learning, but the models proposed so far are not identifiable. Here, we first propose a new intuitive principle of unsupervised deep learning from time series which uses the nonstationary structure of the data. Our l…