并行的NTRU格规约算法

在高维NTRU格中,BKZ算法为了获取较好的规约效果不得不采用大分块,但同时也使运行时间急剧增加。设计了一种msBKZ规约算法,对一组初始基左乘随机幺模矩阵变换出多组基,分别采用小块BKZ（k<18）线程规约,筛选出规约效果最好的那组进行“短代替”后作为初始基,重复该过程以此逐步逼近格中的最短向量。实验表明msBKZ比大块BKZ（k=23）的规约效率至少提高一倍。     

Fast dimensionality reduction and simple PCA

A fast and simple algorithm for approximately calculating the principal components (PCs) of a dataset and so reducing its dimensionality is described. This Simple Principal Components Analysis (SPCA) method was used for dimensionality reduction of two high-dimensional image databases, one of handwritten digits and one of handwritten Japanese characters. It was tested and compared with other techniques. On both databases SPCA shows a fast convergence rate compared with other methods and robustness to the reordering of the samples.     

Fast direct computation of modular reduction

A new general algorithm is proposed for computation of the multiprecision modular reduction x mod m. This algorithm has better time estimates than the well-known Montgomery method and much better application characteristics. The algorithm does not require changes in the initial and final values of the arguments. The precomputation time is no more than the time of two multiplications. Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 3–12, July–August, 1999.     

一种新颖的概念格属性约简算法

概念格是知识处理与分析的一个有力的工具,在知识约简方面有着重要的利用。首先给出了概念格中不同类型属性的等价刻画定理,在此基础上得到了一种新颖的概念格属性约简算法,最后通过实例表明了该约简算法的可行性与有效性。     

概念格属性约简的启发式算法

概念格是知识处理与分析的一个有力的工具,在知识约简和数据挖掘方面有着重要的应用。该文首先从概念外延的角度研究了合理刻画属性重要性的指标,并给出了概念格属性约简的判定定理,然后以这些指标作为启发式信息设计了一种新颖的概念格启发式属性约简算法,最后通过实例表明了该约简算法的可行性与有效性。     

High performance lattice reduction on heterogeneous computing platform

The lattice reduction (LR) technique has become very important in many engineering fields. However, its high complexity makes difficult its use in real-time applications, especially in applications that deal with large matrices. As a solution, the modified block LLL (MB-LLL) algorithm was introduced, where several levels of parallelism were exploited: (a) fine-grained parallelism was achieved through the cost-reduced all-swap LLL (CR-AS-LLL) algorithm introduced together with the MB-LLL by Józsa et al. (Proceedings of the tenth international symposium on wireless communication systems, 2013) and (b) coarse-grained parallelism was achieved by applying the block-reduction concept presented by Wetzel (Algorithmic number theory. Springer, New York, pp 323–337, 1998). In this paper, we present the cost-reduced MB-LLL (CR-MB-LLL) algorithm, which allows to significantly reduce the computational complexity of the MB-LLL by allowing the relaxation of the first LLL condition while executing the LR of submatrices, resulting in the delay of the Gram–Schmidt coefficients update and by using less costly procedures during the boundary checks. The effects of complexity reduction and implementation details are analyzed and discussed for several architectures. A mapping of the CR-MB-LLL on a heterogeneous platform is proposed and it is compared with implementations running on a dynamic parallelism enabled GPU and a multi-core CPU. The mapping on the architecture proposed allows a dynamic scheduling of kernels where the overhead introduced is hidden by the use of several CUDA streams. Results show that the execution time of the CR-MB-LLL algorithm on the heterogeneous platform outperforms the multi-core CPU and it is more efficient than the CR-AS-LLL algorithm in case of large matrices.     

改进的概念格属性约简算法

提出了一种改进的启发式算法,与已有启发式算法相比,能从搜索空间中逐次删除冗余属性,避免了对其重要性的重复计算,同时减少对非冗余属性重要性的计算,从而降低了时间复杂度。通过实例表明了该约简算法的可行性与有效性。     

Attribute reduction theory and approach to concept lattice

The concept lattice, also called Galois lattice, was proposed by Wille in 1982[1]. A concept lattice is an ordered hierarchy that is defined by a binary relationship between objects and attributes in a data set. As an efficient tool of data analysis and knowledge processing, the concept lattice has been applied in many fields, such as knowledge en- gineering, data mining, information searches, and software engineering[2―7]. Most of the researches on the concept lattice concentrate on such to…     

区间值信息系统的概念格约简方法

区间值信息系统是属性值取值为区间值形式的一种特殊信息系统。通过把区间值信息系统转化为0-1形式背景,利用概念格属性约简方法,区间值信息系统协调集的判定定理,并引入可辨识属性矩阵,研究区间值信息系统上基于概念格属性约简的理论方法。     

Fast accurate fuzzy clustering through data reduction

Clustering is a useful approach in image segmentation, data mining, and other pattern recognition problems for which unlabeled data exist. Fuzzy clustering using fuzzy c-means or variants of it can provide a data partition that is both better and more meaningful than hard clustering approaches. The clustering process can be quite slow when there are many objects or patterns to be clustered. This paper discusses the algorithm brFCM, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting the partition quality. The reduction is done by aggregating similar examples and then using a weighted exemplar in the clustering process. The reduction in the amount of clustering data allows a partition of the data to be produced faster. The algorithm is applied to the problem of segmenting 32 magnetic resonance images into different tissue types and the problem of segmenting 172 infrared images into trees, grass and target. Average speed-ups of as much as 59-290 times a traditional implementation of fuzzy c-means were obtained using brFCM, while producing partitions that are equivalent to those produced by fuzzy c-means.     

概念格理论属性约简算法研究

概念格是根据数据集中对象与属性之间的二元关系建立的一种概念层次结构,生动简洁地体现了概念之间的泛化和特化关系。利用概念格的这一特性,提出利用概念格理论进行故障诊断时的属性约简。为验证属性约简的正确性,将约简结果送到神经网络中进行故障诊断,诊断结果表明:利用概念格理论所得到的核心属性和相对必要属性可以对现有故障类型进行正确辨识,降低了故障诊断参数的维数,有利于加快诊断算法的运算速度。     

基于一一映射的概念格属性约简算法*

首先给出了基于一一映射的概念格不同类型属性的等价刻画定理,在此基础上得到了一种新颖的概念格属性约简算法;最后通过实例表明了该约简算法的可行性与有效性。     

基于“桶”分类的快速属性约简算法

粗糙集中的属性约简算法如PawlakZ约简算法及Skowron算法均可有效找到约简的属性集,但都要花费巨大的时间、空间,且对于大数据集约简几乎不可能。针对以上经典算法的不足,文章提出一种快速属性约简算法。算法先将所有样本按决策属性分类,且用不同颜色的桶来装样本。其次,通过深度搜索结合广度搜索的策略,利用几个条件属性集的交集划分得到数据集的约简。在运算过程中,不断地删除交集中的数据,可有效提高效率。实验表明,该算法切实可行,可快速地找到属性约简集,并结合实例,说明了算法的可行性。     

一种快速差别矩阵属性约简算法

为了解决基于差别矩阵属性约简的计算效率问题,分析了基于差别矩阵的属性约简算法的不足,给出了新的差别矩阵的定义,大大减少了差别矩阵中非空元素的个数,提高了属性约简算法的效率。利用单个属性的不可辨识性来计算出现频率最多的属性,进一步降低差别矩阵的大小,并设计了基于新的差别矩阵的快速属性约简算法。对UCI一些数据库进行了仿真,实验结果表明了新算法具有高效性。     

MIMO系统中格点减少检测算法研究*

在实际通信系统中,较差的信道环境会给接收端的信号检测带来较大难度,本文给出了一种结合格点减少的MMSE－SIC检测算法,通过在格点减少的基础上引入最小圴方误差（MMSE）准则以及连续干扰抵消（SIC）的思想,能够仅以多项式的复杂度接近最优检测算法（最大似然,ML）的性能,并且可以通过采用QR分解进一步降低格点减少算法的复杂度。仿真结果表明,在未编码系统中采用基于格点减少的MMSE－SIC算法能够显著提高MIMO系统的误码性能。     

A new parallel lattice reduction algorithm for BKZ reduced bases

In order to implement the original BKZ algorithm in parallel,we describe it in terms of parallelism and give its parallel implementation scheme.Then we analyze the efficiency of algorithm’s parallel implementation and show that the speedup factor of BKZ algorithm in parallel is extremely low.Therefore we present a new parallel lattice reduction algorithm suitable for multiprocessor computer architecture.The new algorithm can obtain a BKZ reduced basis and the parallel speedup is effective.Also with the practical results,although the computational complexity increases compared with the original BKZ algorithm,we still indicate that the new algorithm performs well in parallel and the time cost in parallel is less.At the same time,we show that the length of the shortest vector is smaller.     

基于改进的差别矩阵的快速属性约简算法

为了解决基于差别矩阵属性约简的计算效率问题,首先以计数排序的思想设计了一个新的计算U/C的高效算法,其时间复杂度降为O(|C||U|)。其次分析了基于差别矩阵的属性约简算法的不足,提出了改进的差别矩阵的定义,利用快速计算核属性算法生成的核属性和出现频率最多的属性来降低差别矩阵的大小,并设计了基于改进的差别矩阵的快速属性约简算法,证明了该新算法的时间复杂度和空间复杂度分别被降为max(O|C|2Σ0≤i相似文献   

基于k-ary消减的快速最大公约数算法

最大公约数(GCD)算法中,对于输入B和C,利用Sorenson的右移k-ary消减思想提出一个算法用于寻找整数x和y,使得x和y满足Bx-Cy在二进制表示下低比特位部分为0,即Bx-Cy=0(mod 2^e),其中e是常数正整数。利用该算法能够右移较多比特并大规模降低循环次数。再结合模算法,提出了快速GCD算法,其输入规模为n比特时最差复杂度仍然是O(n²),但最好的情况下复杂度能达到O(nlog²n log logn)。实验数据表明,对于20万以上比特规模的输入,快速GCD算法比Binary GCD算法速度快;对100万比特规模的输入,快速GCD算法速度是Binary GCD算法的两倍。