SLL models labels as they arrive, improving classification performance.
problem Handling dynamic multi-label learning environments with changing labels.
method Label self-representation and two-step approach: regression and ERM.
result SLL generates tighter generalization error bounds for new labels.
Model learns disease self-representations for drug repositioning.
problem Drug repositioning for disease treatment.
method Enforces proximity in disease self-representations to preserve human phenome network structure.
result Method outperforms state-of-the-art approaches and produces biologically interpretable disease self-representations.
GOLFS selects features for clustering by combining global and local information.
problem Feature selection for high-dimensional clustering without labels.
method Combines global and local information via manifold learning and regularized self-representation.
result Improves feature selection and clustering accuracy.
DSL learns discriminative subgraphs from graphs for robust prediction.
problem Learning discriminative subgraphs from graph data for robust prediction.
method Discriminative Subgraph Learning (DSL) framework that enforces sparsity, connectivity, and high discriminative power.
result DSL improves prediction accuracy by up to 16% compared to baselines.
LCRSR recovers latent row space for multi-view clustering.
problem Efficiently recover latent representation from multiple views.
method LCRSR assumes latent representation from multiple views, recovers row space, and determines subspace membership.
result LCRSR recovers complete subspace structure efficiently.
Proposes MVMC for multi-view clustering, enhancing diversity and quality.
problem Leveraging multi-view data for diverse clustering.
method Adapts multi-view self-representation learning, HSIC for redundancy reduction, matrix factorization.
result Generates multiple high-quality and diverse clusterings from multi-view data.
In this paper, we develop a method for unsupervised clustering of two-way (matrix) data by combining two recent innovations from different fields: the Sparse Subspace Clustering (SSC) algorithm [10], which groups points coming from a union of subspaces into their respective subspaces, and the t-product [18], which was …
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.
An algorithm for clustering data from group-invariant subspaces.
problem Clustering data from a union of group-invariant subspaces.
method Sparse Sub-module Clustering (SSmC) based on group-sparse self-representation.
result General conditions for identifying group-invariant subspaces.
Paper proposes S2ConvSCN for robust subspace clustering and classification.
problem Insufficient handling of nonlinear manifolds, data corruptions, and out-of-sample data.
method Self-supervised convolutional subspace clustering network (S2ConvSCN) with FC layer, CIM for robustness, and BD regularization. result Robust S2ConvSCN outperforms baseline on unseen data. DKLM learns adaptive kernels for robust nonlinear subspace clustering.
problem Nonlinear structures in data and challenges with kernel-based clustering.
method Data-driven kernel learning with adaptive weighting and optimal block-diagonal affinity matrix.
result DKLM enhances robustness and preserves manifold structure in nonlinear space.
Proposes CRG_IMSC for better clustering of multi-view data.
problem Lack of effective connectivity in clustering results.
method Directly obtains clustering result with nonnegative constraint; constructs connectivity matrix based on spectral clustering result; uses multiplicative update algorithm.
result Improves clustering performance on benchmark datasets.
New method detects outliers in data with subspaces using sparse representation and random walks.
problem Detecting outliers in data contaminated by subspaces.
method Combines sparse representation with random walks on a graph.
result Correct outlier detection with theoretical guarantees.
Improved clustering of high-dimensional data subspaces with delayed association.
problem Discovering and clustering subspaces in high-dimensional data.
method Joint optimization approach with delayed association for uncertain points.
result Delayed association improves clustering accuracy, especially for intersecting subspaces.
Proposes a method for multi-view clustering that considers local structures and feature weights.
problem Challenges in effectively exploiting complementary information across multiple views.
method Simultaneously assigns weights to different features and captures local information in view-specific feature spaces.
result Achieves state-of-the-art performance on benchmark datasets.
Efficient subspace clustering using Kronecker product reduces computational complexity.
problem Efficiency and scalability issues in traditional subspace clustering methods for large datasets.
method Proposes a subspace clustering model based on the Kronecker product to reduce computational complexity.
result Significantly improved efficiency compared to state-of-the-art methods on public datasets.
New model reconstructs networks by identifying regular components.
problem Uncovering the complexity of network structures.
method Low-rank pursuit based self-representation network model.
result Reconstructs networks and measures their regulability.
iSearch uses innovation directions for robust PCA and outlier detection.
problem Robust PCA and outlier detection in data with outliers.
method iSearch uses innovation directions to compute optimal data points and identify outliers.
result iSearch provides robust PCA and outlier detection with performance guarantees.
Proposes a new model for noisy labels considering multiple labelers and adversarial attacks.
problem Real-world noisy label models with multiple labelers and adversarial attacks.
method Labeler-dependent noise model with adversarial attack vectors.
result State-of-the-art approaches for learning from noisy labels are defeated by adversarial label attacks.
Active learning with weak and strong labelers reduces label queries.
problem Learning from weak and strong labelers with low error.
method Active learning algorithm for weak and strong labelers, statistical consistency, label complexity analysis.
result Reduces label queries compared to using strong labelers alone.
Label smoothing improves model performance even with noisy labels.
problem Mitigating label noise in deep learning models.
method Examined label smoothing as a technique to cope with label noise and compared it to loss-correction methods.
result Label smoothing is competitive with loss-correction techniques under label noise and beneficial for distillation from noisy data.
Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.
problem Tackles noisy supervision in multi-label learning with partially valid labels.
method Develops a two-stage method that estimates label enrichment and ground-truth confidences.
result Demonstrates improved performance over state-of-the-art PML methods.
CbMLC improves multi-label classification with noisy labels.
problem Evaluating multi-label classifiers with noisy labels.
method Context-Based Multi-Label Classifier (CbMLC) that handles noisy labels without additional supervision.
result CbMLC yields substantial improvements over previous methods in noisy label settings.
LNEMLC embeds label network for multi-label classification.
problem Lack of effective adaptation and preservation of generalization abilities for unseen label combinations.
method LNEMLC embeds label network to extend input space for any base multi-label classifier.
result Statistically significant improvements over simple kNN baseline classifier.
Proposes ML-GCN for multi-label network node representation learning.
problem Complex multi-label networks with correlated labels.
method Two Siamese GCNs model node-label and label-label interactions, integrated under a unified objective function.
result Effective node representation learning with preserved label interactions.
Proposes MGPLL for PL learning with non-random noise.
problem Partial label learning with non-random label noise.
method Bi-directional mapping framework, conditional noise label generation, multi-class predictor, adversarial learning.
result Demonstrates state-of-the-art performance in partial label learning.
Logistic regression can handle noisy labels effectively when labels are imperfectly assigned by multiple experts.
problem Label noise in supervised classification due to manual labelling by multiple experts.
method Using approximate posterior probabilities of class membership from multiple experts to train logistic regression models.
result Logistic regression can be robust to label noise when classification difficulty is the only source of errors.
A new method learns label correlations for better multi-label predictions.
problem Label correlations not accurately characterized by existing approaches.
method Sparse reconstruction in the label space to learn correlations, then integrate into model training.
result Our approach outperforms state-of-the-art multi-label learning methods.
Paper tackles multi-label zero-shot learning, improving label embedding projection for unseen classes.
problem Challenges in transferring knowledge from seen to unseen classes in multi-label zero-shot learning.
method Proposes a transfer-aware embedding projection approach to project label embeddings into a low-dimensional space for better inter-label relationships and explicit information transfer.
result Demonstrates the efficacy of the proposed approach through experiments on zero-shot multi-label image classification.
FLAME auto-labels mobile data efficiently on diverse processors.
problem Accurately and efficiently labeling mobile data with unknown labels on heterogeneous processors.
method Self-adaptive auto-labeling system Flame that schedules and executes workloads on mobile processors.
result Flame achieves high labeling accuracy and performance on heterogeneous mobile processors.
PML-LFC improves PML by estimating label confidence from both feature and label spaces.
problem PML challenges in real-world scenarios where only some labels are relevant.
method PML-LFC estimates label confidence using feature and label space similarities, training a predictor with these values.
result PML-LFC achieves superior performance on synthetic and real-world datasets.
Proposes methods to improve multi-label learning by addressing local label imbalance.
problem Local label imbalance within minority class examples degrades multi-label learning performance.
method Introduces a measure to assess local label imbalance and two sampling approaches (MLSOL, MLUL) to address it.
result Experimental results show MLSOL and MLUL improve performance on multi-label datasets.
Efficient multi-label classifier handles missing labels and large datasets.
problem Handling large-scale datasets with many instances and labels, missing label assignments, label correlations, and unlabeled data.
method Non-linear embedding of label vectors using a stochastic approach to predict tail labels, handling missing labels, and exploiting unlabeled data.
result Our method outperforms state-of-the-art multi-label classifiers in prediction performance and training time.
LaMP neural networks model label interactions for multi-label classification.
problem Efficiently modeling label interactions in multi-label classification.
method Label Message Passing (LaMP) Neural Networks, treating labels as nodes on a graph, compute hidden representations conditioned on input using attention-based message passing.
result Significantly outperforms state-of-the-art multi-label classification models on seven real-world datasets.
Paper tackles label insufficiency and inaccuracy in semi-supervised learning.
problem Label insufficiency and inaccuracy in semi-supervised learning.
method Graph-based propagation for label insufficiency and label filtering for inaccuracy.
result SIIS improves performance in the presence of label noise and scarcity.
An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. This paper addresses one such problem, namely how to exploit hierarchical structures over labels. We present a novel method to learn vector representations of a label space give…
PrML exploits label relationships using privileged information and low-rank constraints.
problem Improving multi-label learning performance by leveraging implicit and explicit label connections.
method Generates privileged label features and integrates them into low-rank based multi-label learning framework.
result PrML significantly improves multi-label learning performance compared to competing methods.
New algorithm for XMC from aggregated labels.
problem Finding relevant labels for inputs from a large label universe.
method Developed a scalable algorithm to impute individual labels from group labels.
result Advantages over existing approaches in XMC and MIML tasks.
Enhances labels from unlabeled data using sample correlations.
problem Lack of label distributions in real-world applications.
method Proposes LESC and gLESC methods to enhance label distributions.
result Improves performance of label enhancement through sample correlations.
Retraining with predicted labels improves model accuracy in noisy settings.
problem Improving model accuracy with noisy or corrupted labels.
method Retraining with predicted hard labels in a linearly separable binary classification setting.
result Retraining with predicted labels can increase model accuracy, as proven theoretically.
A fast method reduces label space for online multi-label classification.
problem Challenging multi-label classification with many labels and latent dependencies.
method Linear label space dimension reduction and pseudo-label training.
result Effective in terms of running times and prediction performance.
This paper uses Bayesian networks to analyze label correlations for improving multi-label classifier chains.
problem Improving multi-label classifier chain performance by discovering label correlations and determining the label order.
method Bayesian network (BN) to model label correlations, scoring function to evaluate BN structure, heuristic algorithm to optimize BN, topological sorting to derive label order.
result The proposed BNCC method enhances multi-label classifier chain performance through optimized label order and correlation analysis.
A new method reduces noise in multi-label data and reduces dimensionality.
problem Handling noisy multi-label data in semi-supervised settings.
method Semi-supervised and multi-label dimensionality reduction method using label propagation.
result NMLSDR outperforms state-of-the-art algorithms in reducing noise and dimensionality.
Proposes candidate labeling for more efficient use of crowdsourced labels.
problem Inefficiency of standard crowd learning where annotators provide single labels.
method Allows annotators to provide multiple labels per instance.
result Candidate labeling extracts more knowledge from labelers than standard approach.
This work analyzes two methods for combining multiple binary labels in bipartite ranking.
problem Combining multiple binary labels for optimal bipartite ranking.
method Loss aggregation vs. label aggregation approaches.
result Label aggregation is preferable to loss aggregation due to label dictatorship issues.
DM2L tackles missing labels in multi-label learning by modeling local and global rank structures.
problem Missing labels in multi-label learning.
method DM2L imposes local low-rank structures and global high-rank structures on predictions of instances from the same and different labels, respectively.
result DM2L outperforms state-of-the-art methods in multi-label learning with missing labels.
Paper proposes LAHA to improve XMTC by integrating document content and label correlation.
problem Challenges in tagging documents with most relevant labels from a large label set.
method Hybrid attention deep neural network model (LAHA) that combines multi-label self-attention and adaptive fusion strategies.
result LAHA outperforms state-of-the-art methods, especially on tail labels.
Label noise in adversarial training leads to robust overfitting, explained and mitigated.
problem Label noise in adversarial training causes robust overfitting.
method Proposed a method to automatically calibrate labels.
result Consistent performance improvements across various models and datasets.