Radiomic approaches have achieved promising performance in prediction of clinical outcomes of cancer patients. Particularly, feature dimensionality reduction plays an important role in radiomic studies. However, conventional feature dimensionality reduction techniques are not equipped to suppress data noise or utilize latent supervision information of patient data under study (e.g. difference in patients) for learning discriminative low dimensional representations. To achieve feature dimensionality reduction with improved discriminative power and robustness to noisy radiomic features, we develop an adaptive sparsity regularization based collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively. Our method is built on adaptive sparsity regularized matrix tri-factorization for simultaneous feature denoising and dimension reduction so that the noise is adaptively isolated from the features, and grouping information of patients with distinctive features provides latent supervision information to guide feature dimension reduction. The sparsity regularization is grounded on distribution modeling of transform-domain coefficients in a Bayesian framework. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and empirical results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.
Keywords: Sparsity; collaborative clustering; radiomics; unsupervised learning.