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Found 24 papers, 14 papers with code
Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images
The state-of-the-art (SOTA) methods usually rely on superpixels, however, they do not fully utilize the spatial and spectral information in HSI 3-D structure, and their optimization targets are not clustering-oriented.
Towards Calibrated Deep Clustering Network
Deep clustering has exhibited remarkable performance; however, the overconfidence problem, i. e., the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy, has been overlooked in prior research.
RankMatch: A Novel Approach to Semi-Supervised Label Distribution Learning Leveraging Inter-label Correlations
This paper introduces RankMatch, an innovative approach for Semi-Supervised Label Distribution Learning (SSLDL).
Complementary Classifier Induced Partial Label Learning
complementary labels), which accurately indicates a set of labels that do not belong to a sample.
Data Augmentation For Label Enhancement
Existing LE approach have the following problems: (\textbf{i}) They use logical label to train mappings to LD, but the supervision information is too loose, which can lead to inaccurate model prediction; (\textbf{ii}) They ignore feature redundancy and use the collected features directly.
Label Distribution Learning from Logical Label
To solve the above problems, we propose a novel method to learn an LDL model directly from the logical label, which unifies LE and LDL into a joint model, and avoids the drawbacks of the previous LE methods.
Inaccurate Label Distribution Learning
The previous LDL methods all assumed the LDs of the training instances are accurate.
EGRC-Net: Embedding-induced Graph Refinement Clustering Network
To begin, we leverage both semantic and topological information by employing a vanilla auto-encoder and a graph convolution network, respectively, to learn a latent feature representation.
A Parameter-free Nonconvex Low-rank Tensor Completion Model for Spatiotemporal Traffic Data Recovery
In this study, we proposed a Parameter-Free Non-Convex Tensor Completion model (TC-PFNC) for traffic data recovery, in which a log-based relaxation term was designed to approximate tensor algebraic rank.
Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification
By taking advantage of the local manifold structure, a Laplacian graph is constructed from the superpixels to ensure that a typical pixel should be similar to its neighbors within the same superpixel.
Multi-label Classification with High-rank and High-order Label Correlations
Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization.
Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation
In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix.
Ensemble Clustering via Co-association Matrix Self-enhancement
By propagating the highly-reliable information of the HC matrix to the CA matrix and complementing the HC matrix according to the CA matrix simultaneously, the proposed method generates an enhanced CA matrix for better clustering.
Deep Attention-guided Graph Clustering with Dual Self-supervision
Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cluster assignments, resulting performance limitation.
Adaptive Attribute and Structure Subspace Clustering Network
In this paper, we propose a novel adaptive attribute and structure subspace clustering network (AASSC-Net) to simultaneously consider the attribute and structure information in an adaptive graph fusion manner.
Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification
Specifically, by utilizing the local spatial information and incorporating the predictions from a typical classifier, the first module segments pixels of an input HSI (or its restoration generated by the second module) into superpixels.
Attention-driven Graph Clustering Network
The combination of the traditional convolutional network (i. e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature.
Self-supervised Symmetric Nonnegative Matrix Factorization
Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering results, we propose self-supervised SNMF (S$^3$NMF), which is capable of boosting clustering performance progressively by taking advantage of the sensitivity to initialization characteristic of SNMF, without relying on any additional information.
Clustering Ensemble Meets Low-rank Tensor Approximation
The existing clustering ensemble methods generally construct a co-association matrix, which indicates the pairwise similarity between samples, as the weighted linear combination of the connective matrices from different base clusterings, and the resulting co-association matrix is then adopted as the input of an off-the-shelf clustering algorithm, e. g., spectral clustering.
Superpixel Segmentation Based on Spatially Constrained Subspace Clustering
Consequently, we devise a spatial regularization and propose a novel convex locality-constrained subspace clustering model that is able to constrain the spatial adjacent pixels with similar attributes to be clustered into a superpixel and generate the content-aware superpixels with more detailed boundaries.
Maximum Entropy Subspace Clustering Network
Furthermore, we design a novel framework to explicitly decouple the auto-encoder module and the self-expressiveness module.
Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation
On the basis of the novel tensor low-rank norm, we formulate MVSC as a convex low-rank tensor recovery problem, which is then efficiently solved with an augmented Lagrange multiplier based method iteratively.
Hyperspectral Image Classification via Sparse Representation With Incremental Dictionaries
In this letter, we propose a new sparse representation (SR)-based method for hyperspectral image (HSI) classification, namely SR with incremental dictionaries (SRID).
Clustering-aware Graph Construction: A Joint Learning Perspective
Graph-based clustering methods have demonstrated the effectiveness in various applications.
