Description: This paper presents a clustering approach
which estimates the specific subspace and the intrinsic dimension of each class. Our approach
adapts the Gaussian mixture model framework to high-dimensional data and estimates
the parameters which best fit the data. We obtain a robust clustering method called High-
Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images
in a probabilistic framework. Experiments on a recently proposed database demonstrate the
effectiveness of our clustering method for category localization. Platform: |
Size: 193536 |
Author:tra ba huy |
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Description: Adaptive Grids for Clustering Massive Data Sets - MAFIA. It is a subspace clustering algorithm.-Adaptive Grids for Clustering Massive Data Sets- MAFIA. It is a subspace clustering algorithm. Platform: |
Size: 137216 |
Author:volkanbaykan |
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Description: Clustering through Decision Tree Construction - CLTree - A subspace clustering algorithm.-Clustering through Decision Tree Construction- CLTree- A subspace clustering algorithm. Platform: |
Size: 89088 |
Author:volkanbaykan |
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Description: 关于吸引传播算法的扩展应用的一片文章,对于图像研究很有启发意义-Subspace segmentation is the task of segmenting data
lying on multiple linear subspaces. Its applications in
computer vision include motion segmentation in video,
structure-from-motion, and image clustering. In this work,
we describe a novel approach for subspace segmentation
that uses probabilistic inference via a message-passing al-
gorithm. Platform: |
Size: 3074048 |
Author:fanhaixiong |
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Description: 过去的几年见证了一个explo比如来源和形式。例如,数以百万计的摄像机被安装在建筑物、街道、机场、城市和世界各地。这造成了巨大的进步如何获取、压缩、存储、传输和处理大量复杂的高维数据。-he past few years have witnessed an explo- ple sources and modalities. For example,millions of cameras have been installed in buildings, streets, airports, and cities around the world. This has generated extraordinary advances on how to acquire, compress, store, transmit, and process massive amounts of complex high-dimensional data. Platform: |
Size: 2469888 |
Author:叶新 |
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Description: 数学方法是部分子空间法，用于时频分析算法，包含特征值与特征向量的提取、训练样本以及最后的识别，研究生时的现代信号处理的作业，现代信号处理中谱估计在matlab中的使用，可实现对二维数据的聚类。- Mathematics is part of the subspace, For time-frequency analysis algorithm, Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, Modern signal processing jobs when the graduate, Modern signal processing used in the spectral estimation in matlab, Can realize the two-dimensional data clustering. Platform: |
Size: 8192 |
Author:tswnej |
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Description: 用MATLAB实现动态聚类或迭代自组织数据分析，在MATLAB中求图像纹理特征，可以得到很精确的幅值、频率、相位估计，从先验概率中采样，计算权重，滤波求和方式实现宽带波束形成，数学方法是部分子空间法。
- Using MATLAB dynamic clustering or iterative self-organizing data analysis, In the MATLAB image texture feature, You can get a very accurate amplitude, frequency, phase estimation, Sampling a priori probability, calculate the weight, Filtering summation way broadband beamforming, Mathematics is part of the subspace.
Platform: |
Size: 6144 |
Author:dqaavsrh |
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Description: 现代信号处理中谱估计在matlab中的使用，可实现对二维数据的聚类，数学方法是部分子空间法。- Modern signal processing used in the spectral estimation in matlab, Can realize the two-dimensional data clustering, Mathematics is part of the subspace. Platform: |
Size: 6144 |
Author:sihvmhc |
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Description: 聚类分析是数据挖掘研究领域中一个非常活跃的研究课题) 本文重点分析了高维度数据的自动子空间聚类算法-Cluster analysis is a data mining research area in a very active research topic) This paper focuses on automatic subspace clustering algorithm for high dimensional data Platform: |
Size: 6144 |
Author:汪静 |
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Description: 可实现局部最优近似平面数据聚类，该聚类方法是一种常用的子空间聚类方法，文章作者没有给出相应的源码，这里提供给大家。经过测试可以实现数据聚类，但是对人脸数据集extended yale B效果不理想。参考文献：Teng Zhang, Arthur Szlam, Yi Wang, et al. Hybrid linear modeling via local best-fit flats [J]. International Journal of Computer Vision, 2012, 100: 217-240.-This program can realize local best fit flats subspace clustering. Local best fit flats subspace clustering is one of the commonly used subspace clustering methods. The authors of the paper didn t present the source code and here it is given. The program is tested and it can be used to realize data clustering but its effect is not satisfying in clustering data extended yale B face . Reference: Teng Zhang, Arthur Szlam, Yi Wang, et al. Hybrid linear modeling via local best-fit flats [J]. International Journal of Computer Vision, 2012, 100: 217-240. Platform: |
Size: 2048 |
Author:宋昱 |
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Description: 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 based on density in subspace. CLIQUE divides each dimension into a non overlapping interval, dividing the entire embedded space of the data object into a unit. It uses a density threshold to identify dense and sparse units. A unit is dense, if the number of objects mapped to it is more than the density threshold.) Platform: |
Size: 15360 |
Author:景_天
|
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Description: 子空间聚类算法EWKM (Entropy Weighting K-Means) 在matlab上的实现。(Entropy Weighting K-means which is one of the subspace clustering algorithm written in Matlab.) Platform: |
Size: 1024 |
Author:wind_890 |
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Search - clustering in subspace