Consensus Graph-Based Spectral Ensemble Clustering via Low-Rank Tensor Learning

Ensemble clustering using co-association matrices integrates multiple base clusterings but often overlooks interactions between crucial samples and base clusterings. This neglect can introduce noise and lead to information loss and instability. To address these issues, we propose the Consensus Graph-Based Spectral Ensemble Clustering via Low-Rank Tensor Learning (SECGTL) model. SECGTL organizes base clusterings into a third-order tensor and applies the Fast Fourier Transform (FFT) to capture inter-relations in the frequency domain. By rotating the tensor and minimizing the Tensor Schatten p-norm, SECGTL extracts shared information in a low-rank space, reducing noise and enhancing the learned common graph. With Laplacian rank constraints, SECGTL directly learns a graph with c-connected components, representing the clustering structure without post-processing. Extensive experiments on real-world datasets demonstrate SECGTL's superior performance and robustness to noise.