Multi-view spectral clustering via constrained nonnegative embedding

Multi-view clustering has attracted much attention recently. Among all clustering approaches, spectral ones have gained much popularity thanks to an elaborated and solid theoretical foundation. A major limitation of spectral clustering based methods is that these methods only provide a non-linear projection of the data, to which an additional step of clustering is required. This can degrade the quality of the final clustering due to various factors such as the initialization process or outliers. To overcome these challenges, this paper presents a constrained version of a recent method called Multiview Spectral Clustering via integrating Nonnegative Embedding and Spectral Embedding. Besides retaining the advantages of this method, our proposed model integrates two types of constraints: (i) a consistent smoothness of the nonnegative embedding over all views and (ii) an orthogonality constraint over the columns of the nonnegative embedding. Experimental results on several real datasets show the superiority of the proposed approach.     

Genetic algorithm for text clustering based on latent semantic indexing

In this paper, we develop a genetic algorithm method based on a latent semantic model (GAL) for text clustering. The main difficulty in the application of genetic algorithms (GAs) for document clustering is thousands or even tens of thousands of dimensions in feature space which is typical for textual data. Because the most straightforward and popular approach represents texts with the vector space model (VSM), that is, each unique term in the vocabulary represents one dimension. Latent semantic indexing (LSI) is a successful technology in information retrieval which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. Meanwhile, LSI takes into account the effects of synonymy and polysemy, which constructs a semantic structure in textual data. GA belongs to search techniques that can efficiently evolve the optimal solution in the reduced space. We propose a variable string length genetic algorithm which has been exploited for automatically evolving the proper number of clusters as well as providing near optimal data set clustering. GA can be used in conjunction with the reduced latent semantic structure and improve clustering efficiency and accuracy. The superiority of GAL approach over conventional GA applied in VSM model is demonstrated by providing good Reuter document clustering results.     

结合概率潜在语义分析的文本谱聚类方法研究

传统谱聚类的相似矩阵建立在VSM（Vector Space Model）之上,该模型把词看作孤立的单元,没有考虑自然语言中存在大量的同义词、多义词现象。针对这一问题,提出一种用概率潜在语义分析（Probabilistic Latent Semantic Analysis,PLSA）来提取文本中隐含语义信息的方法,并构建文本集的相似矩阵,从语义的角度考虑了文本之间的相关性。实验结果表明,利用该方法得到的聚类精度有较大提高,结果要好于传统的谱聚类算法,从而验证了该方法的有效性。     

基于深度聚类的无监督特征嵌入学习

为了提高无监督嵌入学习对图像特征的判别能力,提出一种基于深度聚类的无监督学习方法。通过对图像的嵌入特征进行聚类,获得图像之间的伪类别信息,然后最小化聚类损失来优化网络模型,使得模型能够学习到图像的高判别性特征。在三个标准数据集上的图像检索性能表明了该方法的有效性,并且优于目前大多数方法。     

改进的基于谱聚类的子空间聚类模型

子空间聚类的目的是将来自不同子空间的数据分割到其本质上所属的低维子空间。现有的基于数据的自我表示和谱聚类的子空间聚类算法将该问题分为两个连续的阶段：首先从高维数据中学习数据的相似性矩阵,然后通过将谱聚类应用于所学相似性矩阵来推断数据的聚类隶属。通过定义一种新的数据自适应稀疏正则项,并将其与结构稀疏子空间聚类（SSSC）模型和改进的稀疏谱聚类（SSpeC）模型相结合,给出了一个新的统一优化模型。新模型利用数据的相似度和聚类指标的相互引导克服了SSpeC稀疏性惩罚的盲目性,并使得相似度具有了判别性,这有利于将不同子空间的数据分为不同类,弥补了SSSC模型只强制来自相同子空间的数据具有相同标签的缺陷。常用数据集上的实验结果表明,所提模型增强了聚类判别的能力,优于一些经典的两阶段法和SSSC模型。     

基于深度图正则化矩阵分解的多视图聚类算法

针对现实社会中由多种表示或视图组成的多视图数据广泛存在的问题,深度矩阵分解模型因其能够挖掘数据的层次信息而备受关注,但该模型忽略了数据的几何结构信息.为解决以上问题,本文提出基于深度图正则化矩阵分解的多视图聚类算法,通过获取每个视图的局部结构信息和全局结构信息在逐层分解中加入两个图正则化限制,保护多视图数据的几何结构信...     

自适应嵌入的半监督多视角特征降维方法

半监督模式下的多视角特征降维方法,大多并未考虑到不同视角间特征投影的差异,且由于缺乏对降维后的低维矩阵的稀疏约束,无法避免噪声和其他不相关特征的影响。针对这两个问题,提出自适应嵌入的半监督多视角特征降维方法。首先,将投影从单视角下相同的嵌入矩阵扩展到多视角间不同的矩阵,引入全局结构保持项;然后,将无标签的数据利用无监督方法进行嵌入投影,对于有标签的数据,结合分类的判别信息进行线性投影;最后,再将两类多投影映射到统一的低维空间,使用组合权重矩阵来保留全局结构,很大程度上消除了噪声及不相关因素的影响。实验结果表明,所提方法的聚类准确率平均提高了约9%。该方法较好地保留了多视角间特征的相关性,捕获了更多的具有判别信息的特征。     

Hierarchical agglomerative clustering procedure

In this paper the hierarchical agglomerative clustering procedure with the dissimilarity coefficient D [HACP, D] and the definite hierarchical clustering procedure [DHACP, D] including some of the published hierarchical clustering methods are introduced. The formal definitions of both of them are given, by means of which the properties of these procedures can be investigated. An example of applying the procedure concerns the classification in paleobiology.     

Improving vector space embedding of graphs through feature selection algorithms

Graph based pattern representation offers a versatile alternative to vectorial data structures. Therefore, a growing interest in graphs can be observed in various fields. However, a serious limitation in the use of graphs is the lack of elementary mathematical operations in the graph domain, actually required in many pattern recognition algorithms. In order to overcome this limitation, the present paper proposes an embedding of a given graph population in a vector space Rⁿ. The key idea of this embedding approach is to interpret the distances of a graph g to a number of prototype graphs as numerical features of g. In previous works, the prototypes were selected beforehand with heuristic selection algorithms. In the present paper we take a more fundamental approach and regard the problem of prototype selection as a feature selection or dimensionality reduction problem, for which many methods are available. With several experiments we show the feasibility of graph embedding based on prototypes obtained from such feature selection algorithms and demonstrate their potential to outperform previous approaches.     

改进的概率潜在语义分析下的文本聚类算法

概率潜在语义分析(PLSA)模型用期望最大化(EM)算法进行参数训练,由于算法参数的随机初始化,致使聚类的效果过度拟合且过分依赖于参数初始值。将潜在语义分析(LSA)模型参数概率化,用以初始化概率潜在语义分析模型的参数,得到的改进算法有效解决了参数随机初始化问题。经实验验证,所提出的方法对文本聚类的归一化互信息(NMI)和准确度都有明显提高。     

参考节点嵌入最短距离估算在图聚类中的应用

为解决社会关系网络图中节点没有坐标值、不能采用传统的欧几里得距离和曼哈坦距离进行聚类的问题,提出采用最短路径算法,来衡量点与点之间的相异度.针对最短路径算法具有时间复杂度大的缺点,引入基于参考节点嵌入的最短距离估算思想来估算两点之间的近似距离.在此基础上,针对DBLP数据集构成的社会关系网络图进行聚类,使用基于划分的k-medoids算法,分别采用以上两种距离算法,比较其优劣.实验证明改进后的算法和最短路径算法中的Dijkstra 算法相比,距离误差率小,时间复杂度大大降低,在提高效率的同时,取得了同样好的聚类效果.     

基于混合概率潜在语义分析模型的Web聚类

在电子商务应用中,为了更好地了解用户的内在特征,制定有效的营销策略,提出一种基于混合概率潜在语义分析（H PLSA）模型的Web聚类算法。利用概率潜在语义分析（PLSA）技术分别对用户浏览数据、页面内容信息及内容增强型用户事务数据建立PLSA模型, 通过对数—似然函数对三个PLSA模型进行合并得到用户聚类的H PLSA模型和页面聚类的H PLSA模型。聚类分析中以潜在主题与用户、页面以及站点之间的条件概率作为相似度计算依据,聚类算法采用基于距离的k medoids 算法。设计并构建了H PLSA模型,在该模型上对Web聚类算法进行验证,表明该算法是可行的。     

面向不完整多视图聚类的深度互信息最大化方法

多视图聚类是无监督学习领域研究热点之一,近年来涌现出许多优秀的多视图聚类工作,但其中大多数方法均假设各视图是完整的,然而真实场景下数据收集过程极容易发生缺失,造成部分视图不完整。同时,很多方法采取传统机器学习方法（即浅层模型）对数据进行特征学习,这导致模型难以挖掘高维数据内的复杂信息。针对以上问题,本文提出一种面向不完整多视图聚类的深度互信息最大化方法。首先利用深度自编码器挖掘各视图深层次的隐含特征,并通过最大化潜在表示间的互信息来学习各视图间的一致性知识。然后,对于不完整视图中的缺失数据,利用多视图的公共潜在表示进行补全。此外,本文采用一种自步学习策略对网络进行微调,从易到难地学习数据集中的样本,得到更加宜于聚类的特征表示。最后,在多个真实数据集上进行实验,验证了本文方法的有效性。     

基于图嵌入的自适应多视降维方法

随着监控摄像头的普及和数据采集技术的快速发展,多视数据呈现出规模大、维度高和多源异构的特点,使得数据存储空间大、传输慢、算法复杂度高,造成“有数据、难利用”的困境。到目前为止,国内外在多视降维方面的研究还比较少。针对这一问题,本文提出一种基于图嵌入的自适应多视降维方法。该方法在考虑视角内降维后数据重构原始高维数据的基础上,提出自适应学习相似矩阵来探索不同视角之间降维后数据的关联关系,学习各视数据的正交投影矩阵实现多视降维任务。本文在多个数据集上对降维后的多视数据进行了聚类/识别实验验证,实验结果表明基于图嵌入的自适应多视降维方法优于其他降维方法。     

融合链接预测相似度矩阵的属性网络嵌入算法

在属性网络中,与节点相关联的属性信息有助于提升网络嵌入各种任务的性能,但网络是一种图状结构,节点不仅包含属性信息还隐含着丰富的结构信息。为了充分融合结构信息,首先通过定义节点的影响力特性、空间关系特征;然后根据链接预测领域基于相似度的定义构建相似度矩阵,将节点二元组中的关联向量映射到相似度矩阵这一关系空间中,从而保留与节点相关的结构向量信息;再基于图的拉普拉斯矩阵融合属性信息和标签特征,将上述三类信息集成到一个最优化框架中;最后,通过二阶导数求局部最大值计算投影矩阵获取节点的特征表示进行网络嵌入。实验结果表明,提出的算法能够充分利用节点二元组的邻接结构信息,相比于其他基准网络嵌入算法,本模型在节点分类任务上取得了更好的结果。     

基于LDA的改进K-means算法在文本聚类中的应用

针对传统K-means算法初始聚类中心选择的随机性可能导致迭代次数增加、陷入局部最优和聚类结果不稳定现象的缺陷,提出一种基于隐含狄利克雷分布(LDA)主题概率模型的初始聚类中心选择算法。该算法选择蕴含在文本集中影响程度最大的前m个主题,并在这m个主题所在的维度上对文本集进行初步聚类,从而找到聚类中心,然后以这些聚类中心为初始聚类中心对文本集进行所有维度上的聚类,理论上保证了选择的初始聚类中心是基于概率可确定的。实验结果表明改进后算法聚类迭代次数明显减少,聚类结果更准确。     

Enhancing subspace clustering based on dynamic prediction

In high dimensional data, many dimensions are irrelevant to each other and clusters are usually hidden under noise. As an important extension of the traditional clustering, subspace clustering can be utilized to simultaneously cluster the high dimensional data into several subspaces and associate the low-dimensional subspaces with the corresponding points. In subspace clustering, it is a crucial step to construct an affinity matrix with block-diagonal form, in which the blocks correspond to different clusters. The distance-based methods and the representation-based methods are two major types of approaches for building an informative affinity matrix. In general, it is the difference between the density inside and outside the blocks that determines the efficiency and accuracy of the clustering. In this work, we introduce a well-known approach in statistic physics method, namely link prediction, to enhance subspace clustering by reinforcing the affinity matrix.More importantly,we introduce the idea to combine complex network theory with machine learning. By revealing the hidden links inside each block, we maximize the density of each block along the diagonal, while restrain the remaining non-blocks in the affinity matrix as sparse as possible. Our method has been shown to have a remarkably improved clustering accuracy comparing with the existing methods on well-known datasets.     

Ensemble clustering by means of clustering embedding in vector spaces

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance.     

Fuzzy spectral clustering with robust spatial information for image segmentation

In recent years, spectral clustering has become one of the most popular clustering algorithms in areas of pattern analysis and recognition. This algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data, and is simple to implement. However, when the image is corrupted by noise, spectral clustering cannot obtain satisfying segmentation performance. In order to overcome the noise sensitivity of the standard spectral clustering algorithm, a novel fuzzy spectral clustering algorithm with robust spatial information for image segmentation (FSC_RS) is proposed in this paper. Firstly, a non-local-weighted sum image of the original image is generated by utilizing the pixels with a similar configuration of each pixel. Then a robust gray-based fuzzy similarity measure is defined by using the fuzzy membership values among gray values in the new generated image. Thus, the similarity matrix obtained by this measure is only dependent on the number of the gray-levels and can be easily stored. Finally, the spectral graph partitioning method can be applied to this similarity matrix to group the gray values of the new generated image and then the corresponding pixels in the image are reclassified to obtain the final segmentation result. Some segmentation experiments on synthetic and real images show that the proposed method outperforms traditional spectral clustering methods and spatial fuzzy clustering in efficiency and robustness.