Learning from Multi-View Multi-Way Data via Structural Factorization Machines

Real-world relations among entities can often be observed and determined by different perspectives/views. For example, the decision made by a user on whether to adopt an item relies on multiple aspects such as the contextual information of the decision, the item's attributes, the user's profile and the reviews given by other users. Different views may exhibit multi-way interactions among entities and provide complementary information. In this paper, we introduce a multi-tensor-based approach that can preserve the underlying structure of multi-view data in a generic predictive model. Specifically, we propose structural factorization machines (SFMs) that learn the common latent spaces shared by multi-view tensors and automatically adjust the importance of each view in the predictive model. Furthermore, the complexity of SFMs is linear in the number of parameters, which make SFMs suitable to large-scale problems. Extensive experiments on real-world datasets demonstrate that the proposed SFMs outperform several state-of-the-art methods in terms of prediction accuracy and computational cost.Comment: 10 page

Online Unsupervised Multi-view Feature Selection

In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view data are usually unlabeled and come from high-dimensional spaces (such as language vocabularies), unsupervised multi-view feature selection is crucial to many applications. However, it is nontrivial due to the following challenges. First, there are too many instances or the feature dimensionality is too large. Thus, the data may not fit in memory. How to select useful features with limited memory space? Second, how to select features from streaming data and handles the concept drift? Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams? To the best of our knowledge, none of the previous works can solve all the challenges simultaneously. In this paper, we propose an Online unsupervised Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via NMF with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help select discriminative features. Instead of storing all the historical data, OMVFS processes the multi-view data chunk by chunk and aggregates all the necessary information into several small matrices. By using the buffering technique, the proposed OMVFS can reduce the computational and storage cost while taking advantage of the structure information. Furthermore, OMVFS can capture the concept drifts in the data streams. Extensive experiments on four real-world datasets show the effectiveness and efficiency of the proposed OMVFS method. More importantly, OMVFS is about 100 times faster than the off-line methods

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common node embedding across all the views of the graph by combining the different views in a specific way. Hub detection, as another essential topic in graph mining has also drawn extensive attentions in recent years, especially in the context of brain network analysis. Both the graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while the hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application

The F-box protein SKP2 mediates androgen control of p27 stability in LNCaP human prostate cancer cells

BACKGROUND: The cyclin-dependent kinase inhibitor p27 is a putative tumor suppressor that is downregulated in the majority of human prostate cancers. The mechanism of p27 down-regulation in prostate cancers in unknown, but presumably involves increased proteolysis mediated by the SCF(SKP2) ubiquitin ligase complex. Here we used the human prostate cancer cell line LNCaP, which undergoes G1 cell cycle arrest in response to androgen, to examine the role of the SKP2 F-box protein in p27 regulation in prostate cancer. RESULTS: We show that androgen-induced G1 cell cycle arrest of LNCaP cells coincides with inhibition of cyclin-dependent kinase 2 activity and p27 accumulation caused by reduced p27 ubiquitylation activity. At the same time, androgen decreased expression of SKP2, but did not affect other components of SCF(SKP2). Adenovirus-mediated overexpression of SKP2 led to ectopic down-regulation of p27 in asynchronous cells. Furthermore, SKP2 overexpression was sufficient to overcome p27 accumulation in androgen arrested cells by stimulating cellular p27 ubiquitylation activity. This resulted in transient activation of CDK2 activity, but was insufficient to override the androgen-induced G1 block. CONCLUSIONS: Our studies suggest that SKP2 is a major determinant of p27 levels in human prostate cancer cells. Based on our in vitro studies, we suggest that overexpression of SKP2 may be one of the mechanisms that allow prostate cancer cells to escape growth control mediated by p27. Consequently, the SKP2 pathway may be a suitable target for novel prostate cancer therapies

A New Species of the Genus Sinomicrurus Slowinski, Boundy and Lawson, 2001 (Squamata: Elapidae) from Hainan Province, China

A new species of the coral snake genus Sinomicrurus is described based on four specimens from southern Hainan Island (three specimens from Tianchi, Jianfengling National Nature Reserve, one specimen from Diaoluoshan National Nature Reserve), Hainan Province, China. Morphologically, the new species is rather similar to Sinomicrurus kelloggi. However, it is distinct from S. kelloggi by the pattern on the head, the head length, head length/width, the number of infralabial scales, number of bands on dorsal body, and number of blotches on the belly

The dynamical stability of W Ursae Majoris-type systems

Theoretical study indicates that a contact binary system would merge into a rapidly rotating single star due to tidal instability when the spin angular momentum of the system is more than a third of its orbital angular momentum. Assuming that W UMa contact binary systems rigorously comply with the Roche geometry and the dynamical stability limit is at a contact degree of about 70%, we obtain that W UMa systems might suffer Darwin's instability when their mass ratios are in a region of about 0.076--0.078 and merge into the fast-rotating stars. This suggests that the W UMa systems with mass ratio $q\leq0.076$ can not be observed. Meanwhile, we find that the observed W UMa systems with a mass ratio of about 0.077, corresponding to a contact degree of about 86% would suffer tidal instability and merge into the single fast-rotating stars. This suggests that the dynamical stability limit for the observed W UMa systems is higher than the theoretical value, implying that the observed systems have probably suffered the loss of angular momentum due to gravitational wave radiation (GR) or magnetic stellar wind (MSW).Comment: 4 pages, 3 figures, published in MNRAS (2006MNRAS.369.2001