Convolutional neural networks cluster multimodal data without labels.
problem Clustering multimodal data without labeled examples.
method Three-stage framework: encoder, self-expressive layer, decoder. Uses distance between reconstruction and input for training.
result Proposed methods significantly outperform state-of-the-art methods on three datasets.
SEED method finds sparse low-rank representations of data.
problem Finding low-rank representations of data efficiently.
method Greedy selection of incoherent vectors to form a basis.
result SEED can exactly represent low-rank matrices and vectors.
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 method learns subspace affinity function for scalable and inductive clustering.
problem Scaling and inductiveness of self-expressiveness methods for high-dimensional data.
method Siamese neural network architecture for metric learning.
result Model scales to larger datasets and clusters out-of-sample data.
A framework for clustering evolving high-dimensional data using LSTM networks.
problem Clustering evolving high-dimensional data with temporal evolution patterns.
method LSTM-ESCM framework exploiting self-expressive trait and LSTM networks.
result The proposed algorithm outperforms other methods in terms of run time and accuracy.
Paper develops a method to identify minimal sample subspaces from limited data.
problem Challenging task of subspace segmentation with minimal sample subspaces.
method Develops a theoretical framework and optimization algorithms for MSS.
result The MSS model can retrieve minimal sample subspaces even when they are heavily intersected.
DCFSC uses a simple auto-encoder for subspace clustering without parameters.
problem Subspace clustering for large-scale high-dimensional datasets.
method Closed-form shallow auto-encoder for data-driven self-expressive layer, no parameters or optimization.
result Significant memory benefits over existing methods on large datasets.
RED-SC improves deep subspace clustering efficiency.
problem Slow convergence in training deep subspace clustering models.
method Residual Encoder-Decoder network with skip-layer connections and self-expressive layer.
result Training converges much faster with RED-SC.
Stochastic Sparse Subspace Clustering improves subspace clustering by reducing over-segmentation through dropout.
problem Over-segmentation in subspace clustering.
method Introducing dropout regularization to enforce denser connections between points from the same subspace.
result Stochastic Sparse Subspace Clustering effectively handles large datasets and reduces over-segmentation.
Proposes a graph learning framework for clustering and semi-supervised classification.
problem Graph-based clustering and semi-supervised classification techniques have shown impressive performance but lack explicit cluster structure.
method Uses self-expressiveness of samples for global structure and adaptive neighbor approach for local structure. Incorporates rank constraint to ensure optimal performance.
result The proposed method achieves better performance than state-of-the-art methods in clustering and semi-supervised classification.
Paper analyzes SSC for data with missing entries, improving performance.
problem Theoretical analysis of SSC with missing data entries.
method Analyzes theoretical guarantees for SSC with incomplete data, projecting zero-filled data onto observation pattern.
result Improves performance of SSC with incomplete data by projecting zero-filled data onto observation pattern.
Paper proposes a deep subspace clustering method using multi-level representations.
problem Deep subspace clustering of images.
method Convolutional autoencoders with multiple fully-connected layers for multi-level representations, loss minimization with iterative updates.
result The method outperforms state-of-the-art methods on real-world datasets.
This paper improves OMP-based sparse subspace clustering with data-adaptive capability.
problem Existing OMP-based approaches lack data adaptiveness, leading to inaccurate data representation.
method Develops a parameter selection process to adjust OMP parameters based on data distribution and introduces a new SEA ratio metric.
result Proposed approach achieves better clustering accuracy, SEA ratio, and representation quality compared to other OMP-based methods.
Paper proposes a new method to learn similarity from data.
problem Learning similarity from data without losing manifold structure.
method Minimizing reconstruction error of kernel matrices.
result Significant improvements in clustering tasks compared to state-of-the-art methods.
Paper proposes a method to learn structure from data similarities.
problem Learning hidden structures in data beyond low-rank and sparsity.
method Explicitly models data relation, retaining pairwise similarities.
result Framework significantly improves performance on clustering and semisupervised classification.
DSCF-Net learns deep features for clustering with robustness and locality preservation.
problem Unsupervised deep representation learning for clustering.
method Integrates robust deep concept factorization, deep self-expressive representation, and adaptive locality preserving feature learning.
result Delivers state-of-the-art performance on public databases.
Develops methods for causal inference in longitudinal data.
problem Estimating Individual Treatment Effects (ITEs) in high-dimensional, time-varying data.
method Causal Dynamic Variational Autoencoder (CDVAE) and long-term counterfactual regression framework.
result CDVAE outperforms baselines and improves state-of-the-art models, approaching oracle performance.
Study quantifies motion dynamics of ankle sprains using biosensor data.
problem Diagnosing chronic ankle instability (CAI) based on objective biomechanical measures.
method Developed a nonlinear subspace clustering method to learn motion patterns from multi-joint coordination.
result Classification accuracy of >70% on CAI vs. normal controls using leave-one-subject-out cross validation.
DS2CF-Net learns hierarchical representations with deep coupled factorization and enriched prior.
problem Learning deep hierarchical representations from data.
method Dual-constrained Deep Semi-Supervised Coupled Factorization Network (DS2CF-Net) with enriched prior.
result DS2CF-Net achieves state-of-the-art performance in representation learning and clustering.