With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortter...With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed,which is based on singular spectrum analysis(SSA) and locality-sensitive hashing(LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend,which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted forprediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.展开更多
In recent years, the nearest neighbor search （NNS） problem has been widely used in various interesting applications. Locality-sensitive hashing （LSH）, a popular algorithm for the approximate nearest neighbor probl...In recent years, the nearest neighbor search （NNS） problem has been widely used in various interesting applications. Locality-sensitive hashing （LSH）, a popular algorithm for the approximate nearest neighbor problem, is proved to be an efficient method to solve the NNS problem in the high-dimensional and large-scale databases. Based on the scheme of p-stable LSH, this paper introduces a novel improvement algorithm called randomness-based locality-sensitive hashing （RLSH） based on p-stable LSH. Our proposed algorithm modifies the query strategy that it randomly selects a certain hash table to project the query point instead of mapping the query point into all hash tables in the period of the nearest neighbor query and reconstructs the candidate points for finding the nearest neighbors. This improvement strategy ensures that RLSH spends less time searching for the nearest neighbors than the p-stable LSH algorithm to keep a high recall. Besides, this strategy is proved to promote the diversity of the candidate points even with fewer hash tables. Experiments are executed on the synthetic dataset and open dataset. The results show that our method can cost less time consumption and less space requirements than the p-stable LSH while balancing the same recall.展开更多
With the explosive growth of Internet information, it is more and more important to fetch real-time and related information. And it puts forward higher requirement on the speed of webpage classification which is one o...With the explosive growth of Internet information, it is more and more important to fetch real-time and related information. And it puts forward higher requirement on the speed of webpage classification which is one of common methods to retrieve and manage information. To get a more efficient classifier, this paper proposes a webpage classification method based on locality sensitive hash function. In which, three innovative modules including building feature dictionary, mapping feature vectors to fingerprints using Localitysensitive hashing, and extending webpage features are contained. The compare results show that the proposed algorithm has better performance in lower time than the naive bayes one.展开更多
为了提高图像检索的准确度和检索效率,提出一种基于卷积神经网络和局部敏感哈希(Locality-Sensitive Hashing,LSH)算法的图像检索算法。使用图像库ImageNet对视觉几何小组16(Visual Geometry Group 16,VGG16)网络进行训练,获取初始化参...为了提高图像检索的准确度和检索效率,提出一种基于卷积神经网络和局部敏感哈希(Locality-Sensitive Hashing,LSH)算法的图像检索算法。使用图像库ImageNet对视觉几何小组16(Visual Geometry Group 16,VGG16)网络进行训练,获取初始化参数。以卷积神经网络为基础,增加哈希层代替VGG16全连接层,获取图像的高维特征向量。利用哈希函数满足p-稳定分布的LSH算法将高维特征向量映射为哈希码,并将相似图像映射到同一个哈希桶中作为粗检候选集,计算并排序候选集中特征向量欧氏距离完成图像检索,从而得到最终的检索结果。实验结果表明,与其他基于不同哈希算法的图像检索算法相比,所提算法具有较高的准确性和较快的检索速度。展开更多
局部敏感哈希算法需要生成足够长的哈希码才能达到理想的检索精度,这导致哈希码携带了大量冗余信息。针对该问题,本文提出一种面向长局部敏感哈希码的压缩编码算法(Compression Coding for Locality-Sensitive Hashing,CCLSH)。该算法...局部敏感哈希算法需要生成足够长的哈希码才能达到理想的检索精度,这导致哈希码携带了大量冗余信息。针对该问题,本文提出一种面向长局部敏感哈希码的压缩编码算法(Compression Coding for Locality-Sensitive Hashing,CCLSH)。该算法基于哈希位的平衡性、相似性结构保留能力和互信息设计了哈希位质量与相关性度量准则,通过融合上述两个准则去除长哈希码中低质量且具有高相关性的冗余哈希位,实现无损压缩的目的。在三个基准数据集上的实验结果表明,CCLSH所压缩的哈希码在检索性能上与原长哈希码保持一致,同时压缩率都达到了90%以上。此外,与经典的压缩和哈希算法相比,该算法展现出更优秀的检索性能。展开更多
基金supported by the Guangdong Innovative Research Team Program(No.201001N0104744201)the State Key Program of the National Natural Science Foundation of China(No.51437006)
文摘With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed,which is based on singular spectrum analysis(SSA) and locality-sensitive hashing(LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend,which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted forprediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.
基金Project supported by the National Natural Science Foundation of China(Grant No.61173143)the Special Public Sector Research Program of China(Grant No.GYHY201206030)the Deanship of Scientific Research at King Saud University for funding this work through research group No.RGP-VPP-264
文摘In recent years, the nearest neighbor search （NNS） problem has been widely used in various interesting applications. Locality-sensitive hashing （LSH）, a popular algorithm for the approximate nearest neighbor problem, is proved to be an efficient method to solve the NNS problem in the high-dimensional and large-scale databases. Based on the scheme of p-stable LSH, this paper introduces a novel improvement algorithm called randomness-based locality-sensitive hashing （RLSH） based on p-stable LSH. Our proposed algorithm modifies the query strategy that it randomly selects a certain hash table to project the query point instead of mapping the query point into all hash tables in the period of the nearest neighbor query and reconstructs the candidate points for finding the nearest neighbors. This improvement strategy ensures that RLSH spends less time searching for the nearest neighbors than the p-stable LSH algorithm to keep a high recall. Besides, this strategy is proved to promote the diversity of the candidate points even with fewer hash tables. Experiments are executed on the synthetic dataset and open dataset. The results show that our method can cost less time consumption and less space requirements than the p-stable LSH while balancing the same recall.
文摘With the explosive growth of Internet information, it is more and more important to fetch real-time and related information. And it puts forward higher requirement on the speed of webpage classification which is one of common methods to retrieve and manage information. To get a more efficient classifier, this paper proposes a webpage classification method based on locality sensitive hash function. In which, three innovative modules including building feature dictionary, mapping feature vectors to fingerprints using Localitysensitive hashing, and extending webpage features are contained. The compare results show that the proposed algorithm has better performance in lower time than the naive bayes one.
文摘为了提高图像检索的准确度和检索效率,提出一种基于卷积神经网络和局部敏感哈希(Locality-Sensitive Hashing,LSH)算法的图像检索算法。使用图像库ImageNet对视觉几何小组16(Visual Geometry Group 16,VGG16)网络进行训练,获取初始化参数。以卷积神经网络为基础,增加哈希层代替VGG16全连接层,获取图像的高维特征向量。利用哈希函数满足p-稳定分布的LSH算法将高维特征向量映射为哈希码,并将相似图像映射到同一个哈希桶中作为粗检候选集,计算并排序候选集中特征向量欧氏距离完成图像检索,从而得到最终的检索结果。实验结果表明,与其他基于不同哈希算法的图像检索算法相比,所提算法具有较高的准确性和较快的检索速度。
文摘局部敏感哈希算法需要生成足够长的哈希码才能达到理想的检索精度,这导致哈希码携带了大量冗余信息。针对该问题,本文提出一种面向长局部敏感哈希码的压缩编码算法(Compression Coding for Locality-Sensitive Hashing,CCLSH)。该算法基于哈希位的平衡性、相似性结构保留能力和互信息设计了哈希位质量与相关性度量准则,通过融合上述两个准则去除长哈希码中低质量且具有高相关性的冗余哈希位,实现无损压缩的目的。在三个基准数据集上的实验结果表明,CCLSH所压缩的哈希码在检索性能上与原长哈希码保持一致,同时压缩率都达到了90%以上。此外,与经典的压缩和哈希算法相比,该算法展现出更优秀的检索性能。
