Partially view-aligned clustering
In this paper, we study one challenging issue in multi-view data clustering. To be specific, for
two data matrices $\mathbf {X}^{(1)} $ and $\mathbf {X}^{(2)} $ corresponding to two views …
two data matrices $\mathbf {X}^{(1)} $ and $\mathbf {X}^{(2)} $ corresponding to two views …
[PDF][PDF] Multi-view Spectral Clustering Network.
Multi-view clustering aims to cluster data from diverse sources or domains, which has drawn
considerable attention in recent years. In this paper, we propose a novel multi-view …
considerable attention in recent years. In this paper, we propose a novel multi-view …
Deep spectral representation learning from multi-view data
Multi-view representation learning (MvRL) aims to learn a consensus representation from
diverse sources or domains to facilitate downstream tasks such as clustering, retrieval, and …
diverse sources or domains to facilitate downstream tasks such as clustering, retrieval, and …
XAI beyond classification: Interpretable neural clustering
In this paper, we study two challenging problems in explainable AI (XAI) and data clustering.
The first is how to directly design a neural network with inherent interpretability, rather than …
The first is how to directly design a neural network with inherent interpretability, rather than …
-Motivated Low-Rank Sparse Subspace Clustering
In many applications, high-dimensional data points can be well represented by low-
dimensional subspaces. To identify the subspaces, it is important to capture a global and …
dimensional subspaces. To identify the subspaces, it is important to capture a global and …
Cu-net: Component unmixing network for textile fiber identification
Image-based nondestructive textile fiber identification is a challenging computer vision
problem, that is practically useful in fashion, decoration, and design. Although deep learning …
problem, that is practically useful in fashion, decoration, and design. Although deep learning …
Multi-material decomposition for single energy CT using material sparsity constraint
Multi-material decomposition (MMD) decomposes CT images into basis material images,
and is a promising technique in clinical diagnostic CT to identify material compositions …
and is a promising technique in clinical diagnostic CT to identify material compositions …
Boosting subspace co-clustering via bilateral graph convolution
Subspace clustering seeks to cluster high-dimensional data lying in a union of low-
dimensional subspaces. It has achieved state-of-the-art results in image clustering, but text …
dimensional subspaces. It has achieved state-of-the-art results in image clustering, but text …
The k-sparse LSR for subspace clustering via 0-1 integer programming
T Yang, S Zhou, Z Zhang - Signal Processing, 2022 - Elsevier
Subspace clustering is a powerful technology for clustering high-dimensional data. Least
squares regression (LSR) is one of the classical subspace clustering models because of the …
squares regression (LSR) is one of the classical subspace clustering models because of the …
Learning with annotation of various degrees
In this paper, we study a new problem in the scenario of sequences labeling. To be exact,
we consider that the training data are with annotation of various degrees, namely, fully …
we consider that the training data are with annotation of various degrees, namely, fully …