Proposes a new sparse graph-regularized SVD for biclustering.
problem Learning blocking structure in high-dimensional data.
method Imposes graph-regularized norm and L0-norm penalties on singular vectors.
result Efficiently captures natural blocking structures in real and simulated data.
Proposes a method for multi-view clustering that integrates consistent and complementary graph regularizers.
problem Multi-view clustering where views have both consistent and complementary information.
method Consistent and complementary graph-regularized multi-view subspace clustering (GRMSC).
result The proposed method outperforms state-of-the-art methods on benchmark datasets.
Paper proposes methods for estimating partially ranked data with graph regularization.
problem Estimating parameters for partially ranked data with missing data.
method Graph regularization in conjunction with Expectation-Maximization algorithm.
result The proposed estimators work well under non-ignorable missing mechanisms.
Sparse Tucker decomposition with graph regularization improves time series forecasting accuracy.
problem High-dimensional time series forecasting with over-parameterization issue.
method Sparse Tucker decomposition and graph regularization for tensor-based model.
result Non-asymptotic error bound and superior performance in numerical experiments.
Improves Graph Convolutional Network performance on citation datasets.
problem Improving Graph Convolutional Network performance on citation datasets.
method Exploring graph regularization and alternative graph convolution approaches.
result Explicit graph regularization was incorrectly rejected by Kipf & Welling (2016).
DiagNet uses adversarial learning and signed graph regularization for better mammography diagnosis.
problem Inadequate data and similarity between benign and cancerous masses in mammography.
method Adversarial learning to generate positive and negative mammograms, signed similarity graph, deep convolutional neural network training.
result DiagNet outperforms state-of-the-art in breast mass diagnosis.
FCMSC combines multi-view data through feature concatenation for improved clustering.
problem Clustering multi-view data with diverse and sometimes incompatible views.
method FCMSC concatenates multi-view data, integrates l2,1-norm, and uses graph regularization to explore consensus and complementary information. result FCMSC outperforms state-of-the-art multi-view clustering methods on six real-world datasets.
Graph regularized autoencoder improves anomaly detection performance.
problem Unsupervised anomaly detection in high-dimensional data.
method Developed a graph regularized autoencoder using MST-based distances.
result Outperforms alternative methods on 20 benchmark anomaly detection datasets.
Distributed learning for related tasks using graph weights.
problem Learning multiple tasks on different machines.
method Weighted averaging of messages with skewing or stepsize control.
result Different tasks can be learned on different machines.
GCN and GPCA are mathematically connected, leading to improved node classification performance.
problem Improving node classification performance in semi-supervised settings.
method Established a mathematical connection between GCN and GPCA, demonstrating their equivalence and using this to design an effective initialization strategy.
result GPCA paired with a simple MLP achieves similar or better performance than GCN on semi-supervised node classification tasks.
A novel graph-regularized CCA approach for datasets with a common source graph.
problem Discovering hidden sources in datasets with common geometry.
method Graph regularizer to encode common sources' geometry in CCA.
result Improved classification performance over competing methods.
GRTR framework uses graph regularization to improve financial forecasting.
problem High computational costs and economic domain knowledge loss in tensor models.
method Graph-Regularized Tensor Regression (GRTR) framework incorporating economic domain knowledge.
result Improved performance in multi-way financial forecasting with reduced computational costs.
Proposes methods to handle missing data in multi-task learning for healthcare.
problem Handling missing features in multi-task learning datasets.
method Plug-in covariance matrix estimators with LASSO and graph regularization.
result Effective for prediction and model estimation in datasets with missing data.
A new method improves graph-based learning for high-dimensional data.
problem Inconsistent high-dimensional learning efficiency of semi-supervised graph regularization.
method Introducing a novel regularization approach involving centering operation.
result Empirical results show improved performance over spectral clustering.
Regularization improves spectral embedding by focusing on the largest blocks.
problem Improving the quality of spectral embedding for graph data.
method Explained the impact of complete graph regularization on spectral embedding of a block model.
result Regularization forces spectral embedding to focus on the largest blocks, making it less sensitive to noise or outliers.
New method clusters multi-view data by squeezing hybrid knowledge.
problem Removal of redundant information and fusion of multi-view features.
method Low-rank subspace multi-view clustering with adaptive graph regularization.
result Our method outperforms state-of-the-art algorithms on multi-view benchmarks.
Graph-RISE learns image embeddings for ultra-fine-grained semantics.
problem Learning image representations for fine-grained semantics.
method Graph-regularized neural graph learning framework.
result Graph-RISE outperforms state-of-the-art on image classification and triplet ranking.
New view: Deep GCNs learn to anti-oversmooth during training.
problem Performance drop in deep GCNs due to oversmoothing.
method Interpreted GCN as MLP + graph regularization, analyzed training process.
result Deep GCNs learn to anti-oversmooth during training, not over-smooth.
Unified taxonomy for graph representation learning.
problem Lack of unified understanding and integration of graph representation learning methods.
method Proposes a Graph Encoder Decoder Model (GRAPHEDM) to unify graph neural networks, network embedding, and graph regularization.
result Unified taxonomy and Graph Encoder Decoder Model (GRAPHEDM) for graph representation learning.
Proposes DIAL-GNN for joint graph structure and embedding learning.
problem Joint learning of graph structure and embeddings.
method Adapted graph regularization, iterative method for graph structure learning.
result Consistently outperforms state-of-the-art baselines in downstream tasks and computational time.
DeepVir uses deep matrix factorization to predict antivirals for COVID-19.
problem Predicting effective antivirals for COVID-19 using known drug-virus associations.
method Graphical deep matrix factorization with HyPALM optimization.
result DeepVir outperforms state-of-the-art techniques in predicting antivirals for COVID-19.
Novel approach for high-dimensional time series prediction with missing values.
problem Handling missing values and scalability in high-dimensional time series data.
method Temporal Regularized Matrix Factorization (TRMF) framework.
result TRMF outperforms existing approaches in time series tasks.
MISC finds multiple independent clusterings in different subspaces.
problem Difficulties in understanding diverse clusterings.
method Two-stage approach using independent subspace analysis and graph regularized semi-nonnegative matrix factorization.
result MISC discovers different clusterings from independent subspaces.
Improved prediction accuracy in matrix factorization using graph-based priors.
problem Graph side-information may not align with latent-feature relations in matrix completion.
method Identify and remove 'contested' edges using graphical lasso approximation, maintaining linear scalability.
result Improved prediction accuracy with fewer graph edges, demonstrating the often inaccurate nature of graph side-information.
New method for efficient graph learning on large graphs.
problem High memory complexity for graph learning on large, non-sparse graphs.
method Approximate large graphs as intersecting communities, using a new graph regularity lemma.
result Efficient graph learning algorithm with linear memory and time complexity.
Regular languages describe contracting geodesics in groups.
problem Characterizing groups with infinite contracting geodesics.
method Analyzing geodesics in Cayley graphs with a contracting property.
result Groups with infinite contracting geodesics are either virtually Z or acylindrically hyperbolic.
Enhances matrix completion with pairwise penalties for latent features.
problem Improving prediction performance in matrix completion.
method Proposes a general optimization framework with non-/convex pairwise penalty functions and develops an efficient algorithm.
result The proposed framework outperforms standard matrix completion methods, especially in scenarios with latent subgroup structures.
The paper explains how data augmentation improves semi-supervised learning efficiency.
problem Improving accuracy from a small fraction of labeled data.
method Data augmentation induces a similarity graph, which is graph-Laplacian-regularized for downstream learning.
result A fast transductive rate of O(1/nL) is achieved, reducing the number of labels needed. Proposes a deep Auto-Encoder-like framework for visual-tactile fusion object clustering.
problem Combining visual and tactile information for better object clustering.
method Deep Auto-Encoder-like Non-negative Matrix Factorization framework, graph regularizer, modality-level consensus regularizer, alternating minimization strategy.
result Improves object clustering performance by leveraging both visual and tactile modalities.
NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.
problem Privacy-preserving data collaboration with reduced reconstruction risk.
method Formulates linear kernel integration, kernelizes it, and introduces graph regularization and centering constraints.
result NKI improves classification accuracy over existing linear integration methods under nonlinear dimensionality reduction.
Dockless bike sharing systems need effective bike flow prediction models.
problem Imbalanced and dynamic use of bikes leads to mandatory rebalancing operations.
method Divide urban area into regions, model spatio-temporal bike flows, extract traffic patterns, and predict bike flows.
result Interpretable bike flow prediction model provides valuable insights into bike flow analysis.
This paper tackles graph translation challenges by predicting both node and edge attributes simultaneously.
problem Challenges in predicting both node and edge attributes in graph translation, especially in interactive, iterative, and asynchronous processes.
method Developed a novel framework integrating both node and edge translations seamlessly, using spectral graph regularization to maintain consistency.
result Demonstrated the effectiveness of the proposed method on both synthetic and real-world application data.
Paper proposes semi-supervised method for dictionary learning.
problem Learning from both labeled and unlabeled data.
method Uses semi-supervised dictionary learning with LLE for manifold preservation.
result Significant improvements over other methods demonstrated.
Develops efficient machine learning methods using piece-wise quadratic approximations.
problem Weaknesses of quadratic error functionals in high-dimensional, noisy data.
method Piece-wise quadratic approximations (PQSQ) for arbitrary sub-quadratic error potentials.
result Orders of magnitude faster computational performance on synthetic and real-life datasets.
Proposes using knowledge graphs to improve probabilistic model estimation.
problem Lack of direct utilization of domain knowledge in regularization methods.
method Knowledge-based distant regularization framework using knowledge graph embeddings.
result Improvement in generalization capability of probabilistic models.
A method identifies domain-general features using causal graph constraints and regularization.
problem Identifying domain-general features without prior knowledge of spurious features.
method Proposes a novel regularization framework based on causal graph constraints.
result Demonstrates effectiveness in both synthetic and real-world data, outperforming state-of-the-art methods.
New method aggregates nodes in sparse graphical models.
problem Estimating edge-sparse graphical models.
method Tree-aggregated graphical lasso (tag-lasso) method.
result Aggregates nodes in a data-driven fashion using a tree.
Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (users) with different edges (pairwise relationships). In this paper, we address the problem of combining different layers of the multi-layer gra…
Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of nonnegative data. Recently, Graph regula…
Improved phone classification accuracy using graph-based regularization.
problem Phone classification with limited labeled data.
method Graph-based semi-supervised learning with stochastic entropic regularization.
result Significantly improved phone classification accuracy with low labeled data.
Characterizes graphs with Lin-Lu-Yau curvature at least one and explores bone-idle graphs.
problem Characterizing graphs with specific curvature properties.
method Study of Ollivier-Ricci curvature and Lin-Lu-Yau curvature, exploration of regular graphs, and exact formula derivation.
result Characterizes edges that are bone-idle in regular graphs and provides a complete characterization of 4-regular bone-idle graphs.
Developed a framework for designing filters in spectral GCNNs with improved performance.
problem Designing effective filters for spectral GCNNs with regularization properties.
method Exploring regularization properties of graph Laplacian and proposing a generalized framework for filter design.
result New filters derived from the framework outperform state-of-the-art techniques in semi-supervised node classification.
Proposes TAGnet, a deep model for clustering tasks.
problem Bottlenecks in sparse coding-based clustering methods.
method Emulates sparse coding pipeline using deep learning, introduces TAGnet.
result TAGnet outperforms state-of-the-art methods.
Propagation-regularization improves GNN performance by infusing extra graph information.
problem The effectiveness of graph Laplacian regularization in GNNs is questioned and improved upon.
method Introducing Propagation-regularization (P-reg) to enhance GNN performance.
result P-reg boosts GNN performance on various tasks across multiple datasets.
New method clusters data subspaces more accurately.
problem Improving subspace clustering for nonlinear data.
method Introduces nonlinear orthogonal NMF with kernel-based updates.
result Enhanced clustering performance compared to existing methods.
New norms derived from box-norm improve multitask learning performance.
problem Improving multitask learning performance in matrix completion and prediction.
method Derived new norms (box-norm, spectral k-support, spectral box-norm) and improved algorithms to compute them.
result New norms provide state-of-the-art performance in matrix completion and multitask learning.
Abstract compares two norms in holomorphic quadratic differentials.
problem Comparing two norms in holomorphic quadratic differentials.
method Comparison between Avila-Gouëzel-Yoccoz norm and Teichmüller norm.
result Comparison of two norms in holomorphic quadratic differentials.
Improved zero-shot learning with graph-based regularization.
problem Transfer knowledge to unknown classes in zero-shot learning.
method Isoperimetric loss for learning map between visual and semantic embeddings, exploiting graph structure.
result Regularization alone outperforms state-of-the-art methods in zero-shot learning benchmarks.