Subspace clustering refers to the task of clustering a collection of points drawn from a high-dimensional space into a union of multiple subspaces that best fits them. State-of-the-art approaches have been proposed for tackling this clustering problem by using the low-rank or sparse optimization techniques. However, most of the traditional subspace clustering methods are developed for single-view data and are not directly applicable to the multi-view scenario. In this paper, we present a Manifold Regularized Multi-view Subspace Clustering (MRMSC) method to better incorporate the correlated and complementary information from different views. MRMSC yields a unified affinity representation by joint optimization across different views. To respect the data manifold locally, the graph Laplacian is constructed to maintain the intrinsic geometrical structure of each view. In the multi-view integration, a sparsity constraint is imposed to the unified affinity representation in order to better reflect the data relationship from multiple views or features. In experiments, we compared the performance of clustering using MRMSC with the single-view and concatenate-multi-view methods on different datasets. The results showed that better clustering performance can be achieved by fusing the multiple features with a unified affinity representation by MRMSC.

Understanding the role of genetics in diseases is a challenging process that has multiple applications within functional genomics and precision medicine. In this paper, we present a general clustering method to identify disease genes under a multi-view setting. First, by incorporating the graph Laplacian of spectral clustering (SC) into the discriminative K-means, we obtain a single-view subspace representation, which is endowed with both discriminant power and geometrical structure information of that data layer. Then, integrating these individual subspaces together on the Grassmann manifold, we can further find a unified low-dimensional representation under the multi-view SC framework. The proposed two-stage method generalizes the single-view discriminative K-means and the multi-view Grassmann clustering, and can directly handle the case where both attribute-based data and interaction-based networks are available, which is extremely useful in biological research. As a case study of disease gene identification, we apply this method to a benchmark dataset that contains nine gene-by-term text profiles. Experimental results show that our method provides competitive results compared to the state-of-art clustering methods, including a similar one that fuses multiple kernels and Laplacians.

This paper deals with time and frequency offset in coordinated multipoint (CoMP) transmission/reception networks by using distributed linear convolutional space-time coding (DLC-STC). We first prove that perfect time synchronization is impractical for CoMP transmission/reception networks. Then the DLC-STC scheme, in which exact time synchronization at the relay nodes is unnecessary, is proposed for the CoMP joint processing mode (CoMP-JP). Finally, we show the detecting method by minimum mean-squared error decision-feedback equalizer (MMSE-DFE) receivers with any frequency offsets. Simulation results show that with MMSE-DFE receivers, the proposed DLC-STC scheme outperforms the delay diversity scheme and the MMSE-DFE receivers can achieve the same diversity orders as the maximum likelihood sequence detection (MLSD) receivers.

Multi-source domain adaptation (MSDA) in remote sensing (RS) scene classification has recently gained significant attention in the visual recognition community. It leverages multiple well-labeled source domains to train a model capable of achieving strong generalization on the target domain with little to no labeled data from the target domain. However, the distribution shifts among multiple source domains make it more challenging to align the distributions between the target domain and all source domains concurrently. Moreover, relying solely on global alignment risks losing fine-grained information for each class, especially in the task of RS scene classification. To alleviate these issues, we present a Multi-Source Subdomain Distribution Alignment Network (MSSDANet), which introduces novel network structures and loss functions for subdomain-oriented MSDA. By adopting a two-level feature extraction strategy, this model attains better global alignment between the target domain and multiple source domains, as well as alignment at the subdomain level. First, it includes a pre-trained convolutional neural network (CNN) as a common feature extractor to fully exploit the shared invariant features across one target and multiple source domains. Secondly, a dual-domain feature extractor is used after the common feature extractor, which maps the data from each pair of target and source domains to a specific dual-domain feature space and performs subdomain alignment. Finally, a dual-domain feature classifier is employed to make predictions by averaging the outputs from multiple classifiers. Accompanied by the above network, two novel loss functions are proposed to boost the classification performance. Discriminant Semantic Transfer (DST) loss is exploited to force the model to effectively extract semantic information among target and source domain samples, while Class Correlation (CC) loss is introduced to reduce the feature confusion from different classes within the target domain. It is noteworthy that our MSSDANet is developed in an unsupervised manner for domain adaptation, indicating that no label information from the target domain is required during training. Extensive experiments on four common RS image datasets demonstrate that the proposed method achieves state-of-the-art performance for cross-domain RS scene classification. Specifically, in the dual-source and three-source settings, MSSDANet outperforms the second-best algorithm in terms of overall accuracy (OA) by 2.2% and 1.6%, respectively.

Gene expression profiles provide hidden biological knowledge and key information that can be used to distinguish different types of cancer. Due to their high dimensionality and redundancy, gene expression data are often preprocessed by dimensionality reduction (DR) methods. Conventional supervised DR methods use only labeled samples to train the model, leading to a limited performance due to small number of labeled samples in the real world. This paper proposes a transductive local Fisher discriminant analysis (TLFDA) method that uses the available unlabeled data in the learning process. On the one hand, the label information is utilized to maximize the inter-class distance in the embedding space. On the other hand, the local structural information of all data samples is taken into consideration to maintain the smoothness property. In this way, the TLFDA provides more discriminative power than state-of-the-art supervised or semi-supervised DR methods, even when the number of labeled samples is very limited. Our experimental results on benchmark GCM and Acute Leukemia datasets show its promising performance on gene expression profile-based cancer classification.