Robust graph learning from noisy data
Learning graphs from data automatically have shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often corrupted, which …
clustering and semisupervised learning tasks. However, real data are often corrupted, which …
A novel multi-modality image fusion method based on image decomposition and sparse representation
Z Zhu, H Yin, Y Chai, Y Li, G Qi - Information Sciences, 2018 - Elsevier
Multi-modality image fusion is an effective technique to fuse the complementary information
from multi-modality images into an integrated image. The additional information can not only …
from multi-modality images into an integrated image. The additional information can not only …
Large-scale multi-view spectral clustering via bipartite graph
In this paper, we address the problem of large-scale multi-view spectral clustering. In many
real-world applications, data can be represented in various heterogeneous features or …
real-world applications, data can be represented in various heterogeneous features or …
Dynamic affinity graph construction for spectral clustering using multiple features
Spectral clustering (SC) has been widely applied to various computer vision tasks, where
the key is to construct a robust affinity matrix for data partitioning. With the increase in visual …
the key is to construct a robust affinity matrix for data partitioning. With the increase in visual …
Self-weighted robust LDA for multiclass classification with edge classes
Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …
Spectral embedded adaptive neighbors clustering
Spectral clustering has been widely used in various aspects, especially the machine
learning fields. Clustering with similarity matrix and low-dimensional representation of data …
learning fields. Clustering with similarity matrix and low-dimensional representation of data …
Fast adaptive K-means subspace clustering for high-dimensional data
In many real-world applications, data are represented by high-dimensional features. Despite
the simplicity, existing K-means subspace clustering algorithms often employ eigenvalue …
the simplicity, existing K-means subspace clustering algorithms often employ eigenvalue …
Multiple kernel clustering with dual noise minimization
Clustering is a representative unsupervised method widely applied in multi-modal and multi-
view scenarios. Multiple kernel clustering (MKC) aims to group data by integrating …
view scenarios. Multiple kernel clustering (MKC) aims to group data by integrating …
Cross-modal subspace learning for fine-grained sketch-based image retrieval
Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap
between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are …
between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are …
Fast spectral clustering learning with hierarchical bipartite graph for large-scale data
Spectral clustering (SC) is drawing more and more attention due to its effectiveness in
unsupervised learning. However, all of these methods still have limitations. First, the method …
unsupervised learning. However, all of these methods still have limitations. First, the method …