Recently spectral clustering has wide application in pattern recognition.

Research on Gene Expression Data Based on Clustering/ classification Technology

基因表达谱聚类/分类技术研究及展望

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Comparison of Three Different Spectral Clustering Methods Based on Graph Segmentation

三种典型的基于图分割的谱聚类方法比较

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The Study and Application of Spectral Clustering Based on Fuzzy Theory

基于模糊理论的谱聚类算法研究与应用

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Experiments show that this method can improve the clustering results of spectral co-clustering documents and words. ( 5) Based on the LDA topic model, this paper proposed Titled-LDA algorithm for multi-document summarization by fusing topic model.

实验表明，该方法能够提高单词-文档谱聚类方法的聚类效果。（5）在LDA主题模型的基础上，提出了基于主题融合的多文档自动摘要算法Titled-LDA。

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Furthermore, we extend ASC for semi-supervised cases and propose a semi-supervised adaptive spectral clustering ( Semi-ASC) and a semi-supervised spectral ensemble clustering algorithm.

在此基础上，将该方法扩展到半监督领域，提出了一种半监督自适应谱聚类算法（Semi-ASC）和半监督谱聚类集成算法。

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The method is composed of the following steps: ( 1) Dynamic time warping ( DTW) based spectral clustering method is used to obtain a small set of samples to initialize the hidden Markov models ( HMMs) of normal behaviors;

该算法包括以下几个主要步骤：（1）通过基于动态时间归整（DTW）的谱聚类方法获取适量的正常行为样本，对正常行为的隐马尔可夫模型（HMM）进行初始化；

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Especially, the method of the spectral clustering based on color space quantization maybe can provide some idea for image segmentation. And the improved Gabor wavelet feature is a supplement for transform coefficients features of characters.

值得一提的是，基于颜色空间量化的谱聚类文本分割方法可以为其它图像分割问题提供思路；改进的Gabor小波特征也是对字符变换系数特征的一个补充。

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The global topology map describe the relationship of rooms, functions and objects attribution based on RFID tags as nodes, build by spectral clustering algorithm. and design the semantic repository.

对于描述房间关系、功能及物品归属关系的全局拓扑地图是以RFID标签为节点，利用谱聚类算法实现，并完成语义信息库设计。

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Some methods have been presented in M3 network, such as randomly division, hyper-planes division, spectral clustering and so on.

M3网络中已采用了随机划分、超平面划分、等分割聚类、谱聚类和基于先验知识等多种数据划分方法。

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Our two improved spectral clustering algorithms use the limited computing resources ( e.g.: memory, CPU) to maximize the accuracy of spectral clustering.

本文提出的两个算法能够在有限的计算资源（例如：CPU和内存）的条件下最大化谱聚类的聚类精确度。

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Spectral clustering method is used to detect the community structure in the PPI network.

利用谱聚类算法来划分PPI网络中的社团结构。

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Firstly, on bisecting K-means is used to quantize image roughly and then we refine the image by improved spectral clustering based weighted distance.

首先利用高效的二分K均值聚类进行粗略量化，然后使用基于加权距离的谱聚类进行再次量化。

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A spectral clustering algorithm based on fuzzy K-harmonic means

基于模糊K-Harmonic Means的谱聚类算法

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Spectral clustering is one of the most popular modern clustering algorithms.

谱聚类算法近几年已经成为当前最流行的聚类算法之一。

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In this thesis, we propose an adaptive spectral clustering ( ASC) algorithm which can effectively deal with the parameter selection problem.

为此，本文提出了一种自适应谱聚类算法（ASC），试图解决谱聚类中的参数选取问题。

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An inter-speaker information ANN method is proposed, analyzing GMM model speaker identification system performance, and a hybrid GMM/ ANN speaker identification system is structured.

通过分析GMM（高斯混合模型）的说话人辨认系统的性能，提出了一种捕捉不同说话人交互信息的人工神经网络（ANN）方法，构成一个GMM/ANN混合说话人辨认系统。

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They are Vector Quantization ( VQ), Hidden Markov Models ( HMM) and Gaussian Mixture Models ( GMM) speaker identification respectively.

采用的方法分别是矢量量化（VQ）识别方法、隐马尔可夫模型（HMM）识别方法、高斯混合模型（GMM）识别方法。

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Gaussian Mixture Model ( GMM) is used to approximate the pdf of sources and results in a practical Expectation Maximum ( EM) algorithm.

采用高斯混合模型（GMM）来逼近源信号的概率密度函数，简化了算法中的积分，导出了一种实用的期望最大算法（EM）算法迭代式。

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To avoid deforming contours directly in a vector-valued space, a Gaussian mixture model ( GMM) is used to describe the statistical distribution of the space and get the boundary and region probabilities.

为了避免直接在多维空间中应用曲线演化模型，采用高斯混合模型（Gaussian mixture model，简称GMM）来描述该特征图像的概率分布，再从分布模型中计算得到每个像素点的区域信息和边界信息。

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GMM is one of the best pattern recognition techniques because of its good performance, simpleness and lower degree of complexity.

说话人识别中有许多先进有效的识别技术，其中高斯混合模型（GMM）由于性能较好、复杂度小、方法简单，是目前最好的说话人识别算法之一。

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