Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods

A Novel Deep Learning Framework to Identify Latent Neuroendophenotypes from Multimodal Brain Imaging Data

The expertise required to ensure adequate treatment for patients with complex cases is significantly deficient, which leads to the high demand for subtyping or clustering analysis on different clinical situations. The identification and refinement of disease-related subtypes will support both medical treatments and pathological research. Clinically, clustering can narrow down the possible causes and provide effective treatment options. However, the clustering on non-invasive multimodal brain imaging data has not been well addressed. In this thesis, we explore this clustering issue using a deep unsupervised embedded clustering (DEMC) method on multimodal brain imaging data. T1-weighted magnetic resonance imaging (MRI) features and resting-state functional MRI-derived brain networks are learned by a sparse autoencoder and a stacked autoencoder separately and then transformed into the embedding space. Then, the K-Means approach was adopted to set the initial center of the deeply embedded clustering structure (DEC) as the centroids, after which DEC clusters with the KL divergence. In the entire processing, the deep embedding and clustering are optimized simultaneously. This new framework was tested on 994 subjects from Human Connectome Project (HCP) and the results show that this new framework has better clustering performance in comparison with other benchmark algorithms

Large Scale Spectral Clustering Using Approximate Commute Time Embedding

Spectral clustering is a novel clustering method which can detect complex shapes of data clusters. However, it requires the eigen decomposition of the graph Laplacian matrix, which is proportion to $O(n^3)$ and thus is not suitable for large scale systems. Recently, many methods have been proposed to accelerate the computational time of spectral clustering. These approximate methods usually involve sampling techniques by which a lot information of the original data may be lost. In this work, we propose a fast and accurate spectral clustering approach using an approximate commute time embedding, which is similar to the spectral embedding. The method does not require using any sampling technique and computing any eigenvector at all. Instead it uses random projection and a linear time solver to find the approximate embedding. The experiments in several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods