搜索资源列表
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- 基于马氏距离度量的局部线性嵌入算法 局部线性嵌入算法(LLE)中常用欧氏距离度量样本间相似度.而 对于图像等高维数据,欧氏距离不能准确体现样本间的相似程度.文中提出基于马氏距离度量的局部线性嵌入算法(MLLE).算法首先从现有样本中学习到一个 马氏度量,然后在LLE算法的近邻选择、现有样本及新样本降维过程中用马氏度量作为相似性度量.将MLLE算法及其它典型的流形学习算法在ORL和 USPS数据库上进行对比实验,结果表明MLLE算法具有良好的识别性能. -Based on local
dct2_watermarking
- 基于二维dct变换算法的数字图像水印技术,内含水印的嵌入及提取-Based on two-dimensional DCT transform algorithm of digital image watermarking technology, embedded watermark embedding and extraction
ApEn
- 计算一维时间序列的近似熵值,纯手工编写,嵌入维度固定在2维,交流交流-Calculate the approximate entropy of one-dimensional time series, hand written, embedded dimension fixed in 2 dimensions
KLPP
- 核局部邻域嵌入算法是一类流形学习算法,可用于数据的降维(Kernel local neighborhood embedding algorithm is a manifold learning algorithm, which can be used to reduce the dimensionality of data)
OLPP
- 在LPP基础上提出的正交局部邻域嵌入算法,相对于LPP算法,性能更好,可用于数据的降维与分类(The orthogonal local neighborhood embedding algorithm based on LPP has better performance than LPP algorithm and can be used to reduce dimension and classify data)
word2vec
- 可将单词嵌入到低维向量空间,以进一步做分析处理。(embedding words in low-dimensional vector space for efficient analysis.)
CLIQUE
- CLIQUE(Clustering In QUEst)是一种简单的基于网格的聚类方法,用于发现子空间中基于密度的簇。CLIQUE把每个维划分成不重叠的区间,从而把数据对象的整个嵌入空间划分成单元。它使用一个密度阈值识别稠密单元和稀疏单元。一个单元是稠密的,如果映射到它的对象数超过该密度阈值。(CLIQUE (Clustering In QUEst) is a simple grid based clustering method for the discovery of clusters bas
2D-FullCohe5.0
- 可以给Abaqus创建的二维平面模型全局嵌入Cohesive单元(Insert Cohesive Element)
ceshi3d
- abaqus中二维模型cohesive单元嵌入插件(Embedded plug-in of two-dimensional model cooperative unit in ABAQUS)
cohesive-3d
- abaqus中3维模型cohesive单元嵌入的插件v(Plug in for embedding cohesiv unit of 3D model in ABAQUS)