一种基于邻域系统密度差异度量的离群点检测算法

针对离群点检测算法LOF在高维离散分布数据集中检测精度较低及参数敏感性较高的问题,提出了基于邻域系统密度差异度量的离群点检测NSD（neighborhood system density difference）算法。相较于传统基于密度的离群点检测方法,NSD算法引入了截取距离的概念。首先计算数据集中对象在截取距离内的邻居点个数;其次计算对象的邻域系统密度;然后将对象的密度与它邻居的密度进行比较,判定目标对象与其邻居趋向于同一簇的程度;最后输出最可能是离群点的对象。将NSD算法与LOF、LDOF、CBOF算法在真实数据集与合成数据集中对比实验发现,NSD算法具有较高的检测准确率和执行效率以及较低的参数敏感性,证明了NSD算法是有效可行的。     

基于图上随机游走的离群点检测算法

离群点检测算法在网络入侵检测、医疗辅助诊断等领域具有十分广泛的应用。针对LDOF、CBOF及LOF算法在大规模数据集和高维数据集的检测过程中存在的执行时间长及检测率较低的问题,提出了基于图上随机游走(BGRW)的离群点检测算法。首先初始化迭代次数、阻尼因子以及数据集中每个对象的离群值;其次根据对象之间的欧氏距离推导出漫步者在各对象之间的转移概率;然后通过迭代计算得到数据集中每个对象的离群值;最后将数据集中离群值最高的对象判定为离群点并输出。在UCI真实数据集与复杂分布的合成数据集上进行实验,将BGRW算法与LDOF、CBOF和LOF算法在执行时间、检测率和误报率指标上进行对比。实验结果表明,BGRW算法能够有效降低执行时间并在检测率及误报率指标上优于对比算法。     

Adaptive Gaussian back-end based on LDOF criterion for language recognition

In order to alleviate the mismatch in model between training and testing samples caused by inter-language variations,adaptive Gaussian back-end based on LDOF criterion was proposed for language recognition.The local distance-based outlier factor (LDOF) criterion was defined to find the appropriate model parameters and dynamically select the training data subset similar to the testing samples from multiple class training sets.Then original back-end was adjusted to obtain a more matched recognition model.Experimental results on NIST LRE 2009 easily-confused language data set show that proposed method achieves an obvious performance improvement on both the equal error rate (ERR) and average decision cost function.