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Found 9 papers, 5 papers with code
Multilayer Graph Approach to Deep Subspace Clustering
Herein, we apply selected linear subspace clustering algorithm to learn representation matrices from representations learned by all layers of encoder network including the input data.
LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections
The method is aimed at, but not limited to, segmentation of low-dimensional medical images, such as color histopathological images of stained frozen sections.
Clustering and classification of low-dimensional data in explicit feature map domain: intraoperative pixel-wise diagnosis of adenocarcinoma of a colon in a liver
Results are supported by a discussion of interpretability using Shapely additive explanation values for predictions of linear classifier in input space and aEFM induced space.
Subspace Clustering via Robust Self-Supervised Convolutional Neural Network
Self-supervised convolutional SC network ($S^2$ConvSCN) addressed this issue through the addition of a fully connected layer (FC) module and a spectral clustering module that, respectively, generate soft- and pseudo-labels.
Robust Self-Supervised Convolutional Neural Network for Subspace Clustering and Classification
Insufficient capability of existing subspace clustering methods to handle data coming from nonlinear manifolds, data corruptions, and out-of-sample data hinders their applicability to address real-world clustering and classification problems.
$\ell_0$-Motivated Low-Rank Sparse Subspace Clustering
In many applications, high-dimensional data points can be well represented by low-dimensional subspaces.
A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to Subspace Clustering
A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering.
Multi-view Low-rank Sparse Subspace Clustering
Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data.
