CGD: Multi-View Clustering via Cross-View Graph Diffusion

Abstract

Graph based multi-view clustering has been paid great attention by exploring the neighborhood relationship among data points from multiple views. Though achieving great success in various applications, we observe that most of previous methods learn a consensus graph by building certain data representation models, which at least bears the following drawbacks. First, their clustering performance highly depends on the data representation capability of the model. Second, solving these resultant optimization models usually results in high computational complexity. Third, there are often some hyper-parameters in these models need to tune for obtaining the optimal results. In this work, we propose a general, effective and parameter-free method with convergence guarantee to learn a unified graph for multi-view data clustering via cross-view graph diffusion (CGD), which is the first attempt to employ diffusion process for multi-view clustering. The proposed CGD takes the traditional predefined graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by 1) capturing the underlying manifold geometry structure of original data points, and 2) leveraging the complementary information among multiple graphs. The final unified graph used for clustering is obtained by averaging the improved view associated graphs. Extensive experiments on several benchmark datasets are conducted to demonstrate the effectiveness of the proposed method in terms of seven clustering evaluation metrics.

Semi‐Supervised Multi‐View Clustering with Weighted Anchor Graph Embedding

“…The study in[ 12 ] proposed a novel model called Graph Structure Fusion (GSF), which designs an objective function to adaptively tune the structure of the global graph. The work in [ 13 ] proposed a novel multi-view clustering method, which learns a unified graph via cross-view graph diffusion (CGD), where the initial value entered is each predefined view-wise graph matrix. To further learn a compact feature representation, the study in [ 14 ] proposed to capture both the shared information and distinguishing knowledge across different views via projecting each view into a common label space and preserve the local structure of samples by using the matrix-induced regularization.…”

Multi-Metric Induced Robust Graph

“…As a result, the neighborhood-based methods tend to be sensitive to noise and data corruption. Various methods are designed to capture the underlying manifold structures of the data [30][31][32][33][34]. To construct a noise-resistant graph, the representation-based graph construction approach adopts the linear regression to generate a set of the edges and compute the corresponding weights.…”

Dual self-paced multi-view clustering

“…To address this issue, a series of multi-view * Corresponding author. clustering (MVC) methods [3,13,14,16,17,27,28,30,31,[34][35][36][37] have been proposed recently and achieve much better clustering results comparing with their single-view counterparts.…”