Description: AP是在数据点的相似度矩阵的基础上进行聚类.对于规模很大的数据集,AP算法是一种快速、有效的聚类方法,这是其他传统的聚类算法所不能及的,-A semi-supervised clustering method based on affinity propagation (AP) algorithm is proposed in this paper. AP takes as input measures of similarity between pairs of data points. AP is an efficient and fast clustering algorithm for large dataset compared with the existing clustering algorithms, such as K-center clustering. But for the datasets with complex cluster structures, it cannot produce good clustering results. It can improve the clustering performance of AP by using the priori known labeled data or pairwise constraints to adjust the similarity matrix. Experimental results show that such method indeed reaches its goal for complex datasets, and this method outperforms the comparative methods when there are a large number of pairwise constraints. Platform: |
Size: 375808 |
Author:lilan |
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Description: 本代码实现基于成对约束的半监督图嵌入算法-Following the intuition that the image variation of
faces can be effectively modeled by low dimensional
linear spaces, we propose a novel linear subspace
learning method for face analysis in the framework of
graph embedding model, called Semi-supervised
Graph Embedding (SGE). This algorithm builds an
adjacency graph which can best respect the geometry
structure inferred from the must-link pairwise
constraints, which specify a pair of instances belong to
the same class. The projections are obtained by
preserving such a graph structure. Using the notion of
graph Laplacian, SGE has a closed solution of an
eigen-problem of some specific Laplacian matrix and
therefore it is quite efficient. Experimental results on
Yale standard face database demonstrate the
effectiveness of our proposed algorithm. Platform: |
Size: 2048 |
Author:刘国胜 |
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Description: 该程序实现对文件夹中的单文件的C或C++源文件进行两两相似度比较，给出相似度百分比，使用时，要求比较的文件以.c或.cpp或.cc为扩展名，并放在c:盘compare文件夹中。程序使用的算法为，快速公共子串算法，可以对2W字符长度的代码进行评判。可用于教学当中，检测同生编程作业独立完成情况。-The program realized a single file folder of C or C++ source files pairwise similarity comparison, the percentage similarity is given, use, require more of the documents. C or. Cpp or. Cc for the extension, and on the c: drive compare folder. The algorithm uses fast Common Substring algorithm can 2W to judge the character length of the code. Which can be used for teaching, testing program with the health situation of working independently. Platform: |
Size: 32768 |
Author:Checkie Chen |
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Description: 本文详细地介绍了k-means算法和一种改进的K-means算法，即基于成对约束的Cop-kmeans算法。该算法结合了Must-Link和Cannot-Link约束，数据对象在这两类约束的“监督”下进行划分。-This paper introduces the k-means algorithm and an improved K-means algorithm, which is based on pairwise constraints Cop-kmeans algorithm. The algorithm combines the Must-Link and the Cannot-Link constraints, these two types of data objects bound in the "supervision" under the division. Platform: |
Size: 15360 |
Author:李呈利 |
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Description: 一本将基于近邻传播算法的半监督聚类的算方法书.对于聚类研究的很有帮助-Abstract: A semi-supervised clustering method based on affinity propagation (AP) algorithm is proposed in this
paper. AP takes as input measures of similarity between pairs of data points. AP is an efficient and fast clustering
algorithm for large dataset compared with the existing clustering algorithms, such as K-center clustering. But for the
datasets with complex cluster structures, it cannot produce good clustering results. It can improve the clustering
performance of AP by using the priori known labeled data or pairwise constraints to adjust the similarity matrix.
Experimental results show that such method indeed reaches its goal for complex datasets, and this method
outperforms the comparative methods when there are a large number of pairwise constraints. Platform: |
Size: 375808 |
Author:fanhaixiong |
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Description: 1. 本程序使用PCA算法对训练图像降维，得到特征图像；
2. 将每一幅图片与平均图像的差值投影到特征向量空间；
3. 计算训练图片投影两两之间的最大欧氏距离distance_max；
4. 将测试图片也投影到特征向量空间，计算它与所有训练图像投影的最小欧氏距离distanceST_min。
5. 设定判别阈值（设为0.3*distance_max，此为测试得到的经验值，不同阈值将影响判别结果）。当distanceST_min>0.3*distance_max时，则认为测试图像不属于和训练图像相同的类，反之，则认为是。
-1 This program uses PCA dimensionality reduction algorithm training images obtained feature image 2 would mean every picture with the image projected onto the difference feature vector space 3. Computing training images projected maximum pairwise Euclidean from distance_max 4. the test image is projected to the feature vector space, with all the training images calculate its minimum Euclidean distance projection distanceST_min. 5 Set judging threshold (set 0.3* distance_max, the experience obtained by the test, different threshold values will affect the determination result). When distanceST_min> 0.3* distance_max, then that test image and the training images do not belong to the same class, on the contrary, it is considered to be. Platform: |
Size: 640000 |
Author:天涯 |
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Description: 图像匹配是图像处理的基本问题，同时也一个难点，本代码是基于直线描述子的场景匹配。-Line segment matching plays an important role in image processing and computer vision, while it remains a challenging task for images under various transformations. In this work, we present a line matching algorithm which considers both the local appearance of lines and their geometric attributes. To overcome the problem of segment fragmentation and geometric variation, we extract lines in the scale space. To depict the local appearance of lines, we design a novel line descriptor called as Line Band Descriptor (LBD) based on the band representation of the Line Support Region (LSR). To uate the pairwise geometric consistencies, we define the pairwise geometric attributes between line pairs. Then we built a relational graph for candidate line matches and employ a spectral technique to solve this matching problem efficiently. The advantages of the proposed algorithm are as follows: (1) it s robust to image transformations because of the multi-scale line detection strategy (2) it s effic Platform: |
Size: 2534400 |
Author:hitxrb |
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Description: 冒泡排序的算法分析与改进
交换排序的基本思想是：两两比较待排序记录的关键字，发现两个记录的次序相反时即进行交换，直到没有反序的记录为止。-Bubble sort algorithm analysis and the basic idea is to improve the exchange of sort: pairwise comparison of key records to be sorted, found the opposite order of the two records that is to be exchanged, until there are no records in reverse order so far. Platform: |
Size: 166912 |
Author:超儿 |
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Description: The code is reasonably fast due to (optional) randomization and full code vectorization. However, as the algorithm needs to compute pairwise point distances, it can be quite memory intensive. If you get out of memory errors, either downsample the input image or somehow decrease the number of non-zero points in it. It can deal with big amount of noise but can have severe problem with occlusions (major axis end points need to be visible) Platform: |
Size: 21504 |
Author:Vu Phan |
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Description: 基于图割方法的图像分割，是一种重要的图像分割个方法。文章是一篇发表在CVPR2014上的文章。-The LSA_TR library is a general optimization algorithm for arbitrary binary pairwise energies. It implements the LSA-TR algorithm. The library includes matlab files along with C++ code for maxflow/mincut. The code was implemented by Lena Gorelick. Platform: |
Size: 64512 |
Author:flywithheart |
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Description: 分解图匹配（Factorized Graph Matching）源码-Graph matching (GM) is a fundamental problem in computer science, and it plays a central role to solve correspondence
problems in computer vision. GM problems that incorporate pairwise constraints can be formulated as a quadratic assignment problem
(QAP). Although widely used, solving the correspondence problem through GM has two main limitations: (1) the QAP is NP-hard and
difficult to approximate (2) GM algorithms do not incorporate geometric constraints between nodes that are natural in computer vision
problems. To address aforementioned problems, this paper proposes factorized graph matching (FGM). FGM factorizes the large
pairwise affinity matrix into smaller matrices that encode the local structure of each graph and the pairwise affinity between edges.
Four are the benefits that follow this factorization: (1) There is no need to compute the costly (in space and time) pairwise affinity
matrix (2) The factorization allows the use of a path-following optimization algorithm Platform: |
Size: 7779328 |
Author:GuoWei |
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Description: 通过感知器算法分类鸢尾花（三类分类），由于感知器算法是二类分类器，所以三类分类需要两两对比。-Classification of iris by perceptron algorithm (three classifications)Since the perceptron algorithm is a class 2 classifier, three classifications require a pairwise comparison. Platform: |
Size: 2048 |
Author:黄瑾 |
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Description: 函数实现了lelief算法。（计算样本的每一个特征的分类权重，选出具有最大权重的一组特征。样本的特征就是基因）。
对权重较大的一组基因进行两两冗余分析。将相关性弱的和强相关中权重大的基因保留，即为特征基因。
已知基因表达谱数据、不同类样本的个数，自行设定分类特征的权重阈值。
对原始数据标准化的方法：（x－基因均值）/基因方差。
样本的类内类间距离采用欧氏距离。-Lelief function implements the algorithm. (Classification weight each feature samples is calculated weight of a selected set of features having the greatest weight. Characteristic is the gene sample). Of the weight of a larger set of pairwise genetic redundancy analysis. The significant correlation between weak and strong correlation in the right gene retention, that is characteristic gene. Number of known gene expression data, samples of different classes, wherein the classification set threshold own weight. Of raw data standardized methods: (x- Gene mean)/variance gene. The between-class distance class of the sample using the Euclidean distance. Platform: |
Size: 985088 |
Author:马梦竹 |
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Search - pairwise algorithm