Because there are many unlabeld high-dimensional data need to be processed, traditional unsupervised feature selection becomes an important and challenging issue in machine learning domain. The graph matrix that makes the selected features rely on high levels of the learned structure is usually constructed by traditionary embedded unsupervised methods. Nevertheless, data from real life usually contain a lot of noise samples and redundant features that make the graph matrix produced by original data be untenable. This paper designs a novel feature selection algorithm to make the graph matrix credible for classifying high-dimensional data. The local structure learning and feature selection are simultaneously employed to determine the graph matrix adaptively. We make graph matrix contain more available data structure information by constraining the graph matrix, thus we use the graph matrix learning to maintain the local structure between the samples, and the proposed method can select significant features. Moreover, we derive an efficient and iterative update method to optimize the problem. The extensive empirical results demonstrate that the proposed method can select the most discriminative features and achieve the best classification performance, compared to the competing methods.
Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with $5\%$ known values left in the datasets.
The functional connectomics study on resting state functional magnetic resonance imaging (rs-fMRI) data has become a popular way for early disease diagnosis. However, previous methods did not jointly consider the global patterns, the local patterns, and the temporal information of the blood-oxygen-level-dependent (BOLD) signals, thereby restricting the model effectiveness for early disease diagnosis. In this paper, we propose a new graph convolutional network (GCN) method to capture local and global patterns for conducting dynamically functional connectivity analysis. Specifically, we first employ the sliding window method to partition the original BOLD signals into multiple segments, aiming at achieving the dynamically functional connectivity analysis, and then design a multi-view node classification and a temporal graph classification to output two kinds of representations, which capture the temporally global patterns and the temporally local patterns, respectively. We further fuse these two kinds of representation by the weighted concatenation method whose effectiveness is experimentally proved as well. Experimental results on real datasets demonstrate the effectiveness of our method, compared to comparison methods on different classification tasks.
Brain functional connectivity analysis on fMRI data could improve the understanding of human brain function. However, due to the influence of the inter-subject variability and the heterogeneity across subjects, previous methods of functional connectivity analysis are often insufficient in capturing disease-related representation so that decreasing disease diagnosis performance. In this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ L1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data. The multi-graph fusion framework automatically learns the connectivity number for every node (i.e., brain region) and integrates all subjects in a unified framework to output homogenous and discriminative representations of all subjects. Experimental results on two real data sets, i.e., fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods.
The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the l
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