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Huang D.,Sun Yat Sen University | Huang D.,South China Agricultural University | Lai J.-H.,Sun Yat Sen University | Lai J.-H.,Guangdong Key Laboratory of Information Security Technology | And 2 more authors.
IEEE Transactions on Knowledge and Data Engineering

Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorporate global information to refine the local links. To address these two limitations, in this paper, we propose a novel ensemble clustering approach based on sparse graph representation and probability trajectory analysis. In particular, we present the elite neighbor selection strategy to identify the uncertain links by locally adaptive thresholds and build a sparse graph with a small number of probably reliable links. We argue that a small number of probably reliable links can lead to significantly better consensus results than using all graph links regardless of their reliability. The random walk process driven by a new transition probability matrix is utilized to explore the global information in the graph. We derive a novel and dense similarity measure from the sparse graph by analyzing the probability trajectories of the random walkers, based on which two consensus functions are further proposed. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach. © 2015 IEEE. Source

Wang C.-D.,Sun Yat Sen University | Wang C.-D.,SYSU CMU Shunde International Joint Research Institute JRI | Lai J.-H.,Sun Yat Sen University | Lai J.-H.,Guangdong Key Laboratory of Information Security Technology | And 2 more authors.
IEEE Transactions on Knowledge and Data Engineering

The availability of many heterogeneous but related views of data has arisen in numerous clustering problems. Different views encode distinct representations of the same data, which often admit the same underlying cluster structure. The goal of multi-view clustering is to properly combine information from multiple views so as to generate high quality clustering results that are consistent across different views. Based on max-product belief propagation, we propose a novel multi-view clustering algorithm termed multi-view affinity propagation (MVAP). The basic idea is to establish a multi-view clustering model consisting of two components, which measure the within-view clustering quality and the explicit clustering consistency across different views, respectively. Solving this model is NP-hard, and a multi-view affinity propagation is proposed, which works by passing messages both within individual views and across different views. However, the exemplar consistency constraint makes the optimization almost impossible. To this end, by using some previously designed mathematical techniques, the messages as well as the cluster assignment vector computations are simplified to get simple yet functionally equivalent computations. Experimental results on several real-world multi-view datasets show that MVAP outperforms existing multi-view clustering algorithms. It is especially suitable for clustering more than two views. © 2015 IEEE. Source

Huang D.,Sun Yat Sen University | Huang D.,Guangdong Key Laboratory of Information Security Technology | Lai J.-H.,Sun Yat Sen University | Wang C.-D.,Sun Yat Sen University | Wang C.-D.,SYSU CMU Shunde International Joint Research Institute JRI

The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability to weight the base clusterings without access to the original data and can be affected significantly by the low-quality, or even ill clusterings. Secondly, they generally focus on the instance level or cluster level in the ensemble system and fail to integrate multi-granularity cues into a unified model. To address these two limitations, this paper proposes to solve the clustering ensemble problem via crowd agreement estimation and multi-granularity link analysis. We present the normalized crowd agreement index (NCAI) to evaluate the quality of base clusterings in an unsupervised manner and thus weight the base clusterings in accordance with their clustering validity. To explore the relationship between clusters, the source aware connected triple (SACT) similarity is introduced with regard to their common neighbors and the source reliability. Based on NCAI and multi-granularity information collected among base clusterings, clusters, and data instances, we further propose two novel consensus functions, termed weighted evidence accumulation clustering (WEAC) and graph partitioning with multi-granularity link analysis (GP-MGLA) respectively. The experiments are conducted on eight real-world datasets. The experimental results demonstrate the effectiveness and robustness of the proposed methods. © 2015 Elsevier B.V. Source

Shi S.-C.,Sun Yat Sen University | Guo C.-C.,Sun Yat Sen University | Guo C.-C.,SYSU CMU Shunde International Joint Research Institute JRI | Lai J.-H.,Sun Yat Sen University | And 3 more authors.

In this work, we present a multi-level adaptive correspondence model for person re-identification. Coarse segmentation and single level representation carry poorly discriminative information for generating a signature of a target, whilst fine segmentation with a fixed matching fashion is hindered severely by misalignment of corresponding body parts. We address such a dilemma through a multi-level adaptive correspondence scheme. Our approach encodes a pedestrian based on horizontal stripes in multi-level to capture rich visual cues as well as implicit spatial structure. Then dynamic correspondence of stripes within an image pair is conducted. Considering that manually selected weights in the final fusion stage is not advisable, we employ RankSVM to seek a data-driven fusion solution. We demonstrate the effectiveness of our method on two public datasets and another new dataset built for single shot re-identification. Comparisons with state-of-the-art re-identification methods show the superior performance of our approach. © 2015 Elsevier B.V. Source

Cai K.,Sun Yat Sen University | Jiang M.,Sun Yat Sen University | Jiang M.,SYSU CMU Shunde International Joint Research Institute JRI
2015 IEEE/CIC International Conference on Communications in China, ICCC 2015

In this paper, we propose a new precoded multi-user (MU) multiple-input multiple-output (MIMO) optical orthogonal frequency division multiplexing (OOFDM) aided visible light communication (VLC) system. Optical spatial modulation (OSM) and spatial pulse position modulation (SPPM) are utilised for providing uniform indoor illumination and high speed data transmissions. Both OSM and SPPM exploit the index of data streams for carrying additional information bits, while the multi-user interference (MUI) occurring between different user equipments (UEs) is mitigated by the MIMO precoder. The performances of the proposed VLC system using OSM and SPPM are evaluated and compared, which demonstrate that the OSM-aided system is capable of achieving high data rates at sufficiently high signal-to-noise ratios (SNRs), while the SPPM-aided system has a higher bandwidth efficiency at low SNRs, despite the fact that its attainable peak data rate is lower than that of the SPPM counterpart. © 2015 IEEE. Source

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