Semi-supervised clustering via multi-level random walk

A key issue of semi-supervised clustering is how to utilize the limited but informative pairwise constraints. In this paper, we propose a new graph-based constrained clustering algorithm, named SCRAWL. It is composed of two random walks with different granularities. In the lower-level random walk, SCRAWL partitions the vertices (i.e., data points) into constrained and unconstrained ones, according to whether they are in the pairwise constraints. For every constrained vertex, its influence range, or the degrees of influence it exerts on the unconstrained vertices, is encapsulated in an intermediate structure called component. The edge set between each pair of components determines the affecting scope of the pairwise constraints. In the higher-level random walk, SCRAWL enforces the pairwise constraints on the components, so that the constraint influence can be propagated to the unconstrained edges. At last, we combine the cluster membership of all the components to obtain the cluster assignment for each vertex. The promising experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our method.     

Semi-supervised graph clustering: a kernel approach

Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vector-based and graph-based approaches. We first show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel k-means objective (Dhillon et al., in Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, 2004a). A recent theoretical connection between weighted kernel k-means and several graph clustering objectives enables us to perform semi-supervised clustering of data given either as vectors or as a graph. For graph data, this result leads to algorithms for optimizing several new semi-supervised graph clustering objectives. For vector data, the kernel approach also enables us to find clusters with non-linear boundaries in the input data space. Furthermore, we show that recent work on spectral learning (Kamvar et al., in Proceedings of the 17th International Joint Conference on Artificial Intelligence, 2003) may be viewed as a special case of our formulation. We empirically show that our algorithm is able to outperform current state-of-the-art semi-supervised algorithms on both vector-based and graph-based data sets.     

结构复杂数据的半监督聚类

基于成对限制,提出一种半监督聚类算法（ＳＣＣＤ）,它能够处理存在多种密度结构复杂的数据且识别任意形状的簇．利用成对限制反映的多密度分布信息计算基于密度的聚类算法（ＤＢＳＣＡＮ）的邻域半径参数Ｅｐｓ,并利用不同参数的ＤＢＳＣＡＮ 算法处理复杂形状且密度变化的数据集．实验结果表明,ＳＣＣＤ 算法能在噪声环境下发现任意形状且多密度的簇,性能优于已有同类算法．     

Distance metric learning guided adaptive subspace semi-supervised clustering

Most existing semi-supervised clustering algorithms are not designed for handling high-dimensional data. On the other hand, semi-supervised dimensionality reduction methods may not necessarily improve the clustering performance, due to the fact that the inherent relationship between subspace selection and clustering is ignored. In order to mitigate the above problems, we present a semi-supervised clustering algorithm using adaptive distance metric learning (SCADM) which performs semi-supervised clustering and distance metric learning simultaneously. SCADM applies the clustering results to learn a distance metric and then projects the data onto a low-dimensional space where the separability of the data is maximized. Experimental results on real-world data sets show that the proposed method can effectively deal with high-dimensional data and provides an appealing clustering performance.     

Spectral clustering: A semi-supervised approach

Recently, graph-based spectral clustering algorithms have been developing rapidly, which are proposed as discrete combinatorial optimization problems and approximately solved by relaxing them into tractable eigenvalue decomposition problems. In this paper, we first review the current existing spectral clustering algorithms in a unified-framework way and give a straightforward explanation about spectral clustering. We also present a novel model for generalizing the unsupervised spectral clustering to semi-supervised spectral clustering. Under this model, prior information given by some instance-level constraints can be generalized to space-level constraints. We find that (undirected) graph built on the enlarged prior information is more meaningful, hence the boundaries of the clusters are more correct. Experimental results based on toy data, real-world data and image segmentation demonstrate the advantages of the proposed model.     

Non-negative matrix factorization for semi-supervised data clustering

Traditional clustering algorithms are inapplicable to many real-world problems where limited knowledge from domain experts is available. Incorporating the domain knowledge can guide a clustering algorithm, consequently improving the quality of clustering. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for data clustering. In SS-NMF, users are able to provide supervision for clustering in terms of pairwise constraints on a few data objects specifying whether they “must” or “cannot” be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the data similarity matrix to infer the clusters. Theoretically, we show the correctness and convergence of SS-NMF. Moveover, we show that SS-NMF provides a general framework for semi-supervised clustering. Existing approaches can be considered as special cases of it. Through extensive experiments conducted on publicly available datasets, we demonstrate the superior performance of SS-NMF for clustering.

基于主动学习策略的半监督聚类算法研究

提出一种选择最富信息数据并予以标记的基于主动学习策略的半监督聚类算法。首先, 采用传统K-均值聚类算法对数据集进行粗聚类; 其次, 根据粗聚类结果计算出每个数据隶属于每个类簇的隶属度, 筛选出满足最大与次大隶属度差值小于阈值的候选数据, 并从中选择差值较小的数据作为最富信息的数据进行标记; 最后, 将候选数据集合中未标记数据分组到与每类已被标记数据平均距离最小的类簇中。实验表明, 提出的主动学习策略能够很好地学习到最富信息数据, 基于该学习策略的半监督聚类算法在测试不同数据集时均获得了较高的准确率。     

一种基于数据相关性的半监督模糊聚类集成方法

现有的半监督聚类集成方法能利用先验信息,使集成的准确性、鲁棒性和稳定性得到提高,但在集成阶段加入成对约束信息时,只考虑了给定的约束信息而忽视了约束点与被约束点的邻域点之间的关系.针对此问题,提出了一种基于数据相关性的半监督模糊聚类集成方法.该方法首先利用半监督模糊聚类算法建立集成信息矩阵,并将其转换为相似性矩阵;然后,利用已知的约束信息及约束点与被约束点的邻域点之间的关系来修改相似性矩阵;最后,利用图划分算法得到最终的聚类结果.真实数据上的实验结果表明,提出的方法可以有效提高聚类质量.     

Two-stage nonparametric kernel leaning: From label propagation to kernel propagation

We introduce a kernel learning algorithm, called kernel propagation (KP), to learn a nonparametric kernel from a mixture of a few pairwise constraints and plentiful unlabeled samples. Specifically, KP consists of two stages: the first is to learn a small-sized sub-kernel matrix just restricted to the samples with constrains, and the second is to propagate this learned sub-kernel matrix into a large-sized full-kernel matrix over all samples. As an interesting fact, our approach exposes a natural connection between KP and label propagation (LP), that is, one LP can naturally induce its KP counterpart. Thus, we develop three KPs from the three typical LPs correspondingly. Following the idea in KP, we also naturally develop an out-of-sample extension to directly capture a kernel matrix for outside-training data without the need of relearning. The final experiments verify that our developments are more efficient, more error-tolerant and also comparably effective in comparison with the state-of-the-art algorithm.     

Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints

Relevant component analysis (RCA) is a recently proposed metric learning method for semi-supervised learning applications. It is a simple and efficient method that has been applied successfully to give impressive results. However, RCA can make use of supervisory information in the form of positive equivalence constraints only. In this paper, we propose an extension to RCA that allows both positive and negative equivalence constraints to be incorporated. Experimental results show that the extended RCA algorithm is effective.     

基于主动学习先验的半监督K-means聚类算法

基于迭代框架的主动半监督聚类框架（IASSCF）是一个流行的半监督聚类框架。该框架存在两个问题：其一,初始先验信息较少导致迭代初期聚类效果不佳,进而影响后续聚类结果;其二,每次迭代只选择信息量最大的一个样本标记,导致运行速度慢、性能提升慢。针对这两个问题,设计了一种基于主动学习先验的半监督K-means聚类算法。该方法包含初始化阶段和迭代阶段。初始化阶段主动选择代表性较高的节点集合,并基于代表节点集合构建各类的先验节点集合和约束先验集合。迭代阶段,每次迭代包含三步：1）基于当前约束先验集合,利用约束半监督聚类算法PCK-means对数据进行聚类;2）依据当前聚类结果,主动选择每个簇中最具价值信息的未标注样本点;3）利用选择样本点扩充先验节点集合及约束集合。迭代此过程至达到收敛阈值。实验结果表明,与基于原IASSCF框架的半监督K-means聚类算法相比,所提算法运行速度更快,性能更优。     

Semi-supervised fuzzy clustering: A kernel-based approach

Semi-supervised clustering algorithms aim to improve the clustering accuracy under the supervisions of a limited amount of labeled data. Since kernel-based approaches, such as kernel-based fuzzy c-means algorithm (KFCM), have been successfully used in classification and clustering problems, in this paper, we propose a novel semi-supervised clustering approach using the kernel-based method based on KFCM and denote it the semi-supervised kernel fuzzy c-mean algorithm (SSKFCM). The objective function of SSKFCM is defined by adding classification errors of both the labeled and the unlabeled data, and its global optimum has been obtained through repeatedly updating the fuzzy memberships and the optimized kernel parameter. The objective function may have more than one local optimum, so we employ a function transformation technique to reformulate the objective function after a local minimum has been obtained, and select the best optimum as the solution to the objective function. Experimental results on both the artificial and several real data sets show SSKFCM performs better than its conventional counterparts and it achieves the best accurate clustering results when the parameter is optimized.     

Semi-supervised model-based document clustering: A comparative study

Semi-supervised learning has become an attractive methodology for improving classification models and is often viewed as using unlabeled data to aid supervised learning. However, it can also be viewed as using labeled data to help clustering, namely, semi-supervised clustering. Viewing semi-supervised learning from a clustering angle is useful in practical situations when the set of labels available in labeled data are not complete, i.e., unlabeled data contain new classes that are not present in labeled data. This paper analyzes several multinomial model-based semi-supervised document clustering methods under a principled model-based clustering framework. The framework naturally leads to a deterministic annealing extension of existing semi-supervised clustering approaches. We compare three (slightly) different semi-supervised approaches for clustering documents: Seeded damnl, Constrained damnl, and Feedback-based damnl, where damnl stands for multinomial model-based deterministic annealing algorithm. The first two are extensions of the seeded k-means and constrained k-means algorithms studied by Basu et al. (2002); the last one is motivated by Cohn et al. (2003). Through empirical experiments on text datasets, we show that: (a) deterministic annealing can often significantly improve the performance of semi-supervised clustering; (b) the constrained approach is the best when available labels are complete whereas the feedback-based approach excels when available labels are incomplete. Editor: Andrew Moore     

Semi-supervised learning combining co-training with active learning

Co-training is a good paradigm of semi-supervised, which requires the data set to be described by two views of features. There are a notable characteristic shared by many co-training algorithm: the selected unlabeled instances should be predicted with high confidence, since a high confidence score usually implies that the corresponding prediction is correct. Unfortunately, it is not always able to improve the classification performance with these high confidence unlabeled instances. In this paper, a new semi-supervised learning algorithm was proposed combining the benefits of both co-training and active learning. The algorithm applies co-training to select the most reliable instances according to the two criterions of high confidence and nearest neighbor for boosting the classifier, also exploit the most informative instances with human annotation for improve the classification performance. Experiments on several UCI data sets and natural language processing task, which demonstrate our method achieves more significant improvement for sacrificing the same amount of human effort.     

改进的半监督模糊聚类算法

针对Ｇｒｉｒａ等近期提出的利用点对约束的半监督模糊聚类算法,其约束项与竞争聚类算法（ＣＡ）的目标函数之间数量级不一致,造成隶属度调整过度的问题,在重新定义目标函数的基础上提出一种改进算法,约束惩罚函数采用约束点对中两个样本新的联合表达式,使数量级与经典模糊聚类算法一致．实验结果显示,新算法的约束项与ＣＡ目标函数之间能很好地协调合作,并能通过对模糊隶属度的适度调整,实现更准确的聚类．     

Data spread-based entropy clustering method using adaptive learning

Clustering analysis is to identify inherent structures and discover useful information from large amount of data. However, the decision makers may suffer insufficient understanding the nature of the data and do not know how to set the optimal parameters for the clustering method. To overcome the drawback above, this paper proposes a new entropy clustering method using adaptive learning. The proposed method considers the data spreading to determine the adaptive threshold within parameters optimized by adaptive learning. Four datasets in UCI database are used as the experimental data to compare the accuracy of the proposed method with the listing clustering methods. The experimental results indicate that the proposed method is superior to the listing methods.     

Semi-supervised kernel canonical correlation analysis with application to human fMRI

Kernel canonical correlation analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations that are more closely tied to the underlying process that generates the data and can ignore high-variance noise directions. However, for data where acquisition in one or more modalities is expensive or otherwise limited, KCCA may suffer from small sample effects. We propose to use semi-supervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces.Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multi-variate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of KCCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing.     

一种改进的半监督聚类入侵检测算法

针对网络环境,提出了一种新的半监督聚类入侵检测算法,将主动学习策略应用于半监督聚类过程中,利用少量的标记数据,生成用于初始化算法的种子聚类,通过辅助聚类过程,根据网络数据的特点,检测已知和未知攻击。主动学习策略查询网络中未标记数据与标记数据的约束关系,对标记数据可以快速获得k个不相交的非空近邻集,经检测结果证明,改进了算法的性能,且表明了算法的可行性及有效性。     

Locally linear metric adaptation with application to semi-supervised clustering and image retrieval

Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised clustering, which performs clustering in the presence of some background knowledge or supervisory information expressed as pairwise similarity or dissimilarity constraints. However, existing metric learning methods for semi-supervised clustering mostly perform global metric learning through a linear transformation. In this paper, we propose a new metric learning method that performs nonlinear transformation globally but linear transformation locally. In particular, we formulate the learning problem as an optimization problem and present three methods for solving it. Through some toy data sets, we show empirically that our locally linear metric adaptation (LLMA) method can handle some difficult cases that cannot be handled satisfactorily by previous methods. We also demonstrate the effectiveness of our method on some UCI data sets. Besides applying LLMA to semi-supervised clustering, we have also used it to improve the performance of content-based image retrieval systems through metric learning. Experimental results based on two real-world image databases show that LLMA significantly outperforms other methods in boosting the image retrieval performance.     

Intelligent photo clustering with user interaction and distance metric learning

Photo clustering is an effective way to organize albums and it is useful in many applications, such as photo browsing and tagging. But automatic photo clustering is not an easy task due to the large variation of photo content. In this paper, we propose an interactive photo clustering paradigm that jointly explores human and computer. In this paradigm, the photo clustering task is semi-automatically accomplished: users are allowed to manually adjust clustering results with different operations, such as splitting clusters, merging clusters and moving photos from one cluster to another. Behind users’ operations, we have a learning engine that keeps updating the distance measurements between photos in an online way, such that better clustering can be performed based on the distance measure. Experimental results on multiple photo albums demonstrated that our approach is able to improve automatic photo clustering results, and by exploring distance metric learning, our method is much more effective than pure manual adjustments of photo clustering.