基于正则化的半监督弱标签分类方法

针对多标签学习中实例标签的缺失补全和预测问题,本文提出一种基于正则化的半监督弱标签分类方法(简称SWCMR),方法同时兼顾实例相似性和标签相关性.SWCMR首先根据标签相关性对弱标签实例的缺失标签进行初步预估,然后利用弱标签实例和无标签实例构造邻域图,从实例相似性和标签相关性角度构建基于平滑性假设的正则化项,接下来利用预估后的弱标签实例结合无标签实例训练半监督弱标签分类模型.在多种公共多标签数据集上的实验结果表明,SWCMR提高了分类性能,尤其是标签信息较少时,分类效果提升更显著.     

基于局部流形重构的半监督多视图图像分类

为了在半监督情境下利用多视图特征中的信息提升分类性能,通过最小化输入特征向量的局部重构误差为以输入特征向量为顶点构建的图学习合适的边权重,将其用于半监督学习。通过将最小化输入特征向量的局部重构误差捕获到的输入数据的流形结构应用于半监督学习,有利于提升半监督学习中标签预测的准确性。对于训练样本图像的多视图特征的使用问题,借助于改进的典型相关分析技术学习更具鉴别性的多视图特征,将其有效融合并用于图像分类任务。实验结果表明,该方法能够在半监督情境下充分地挖掘训练样本的多视图特征表示的鉴别信息,有效地完成鉴别任务。     

Effective human action recognition by combining manifold regularization and pairwise constraints

The ever-growing popularity of mobile networks and electronics has prompted intensive research on multimedia data (e.g. text, image, video, audio, etc.) management. This leads to the researches of semi-supervised learning that can incorporate a small number of labeled and a large number of unlabeled data by exploiting the local structure of data distribution. Manifold regularization and pairwise constraints are representative semi-supervised learning methods. In this paper, we introduce a novel local structure preserving approach by considering both manifold regularization and pairwise constraints. Specifically, we construct a new graph Laplacian that takes advantage of pairwise constraints compared with the traditional Laplacian. The proposed graph Laplacian can better preserve the local geometry of data distribution and achieve the effective recognition. Upon this, we build the graph regularized classifiers including support vector machines and kernel least squares as special cases for action recognition. Experimental results on a multimodal human action database (CAS-YNU-MHAD) show that our proposed algorithms outperform the general algorithms.

     

基于局部聚类与图方法的半监督学习算法

基于图的算法已经成为半监督学习中的一种流行方法, 该方法把数据定义为图的节点, 用图的边表示数据之间的关系, 在各种数据分布情况下都具有很高的分类准确度. 然而图方法的计算复杂度比较高, 当图的规模比较大时, 计算所需要的时间和存储都非常大, 这在一定程度上限制了图方法的使用. 因此, 如何控制图的大小是基于图的半监督学习算法中的一个重要问题. 本文提出了一种基于密度估计的快速聚类方法, 可以在局部范围对数据点进行聚类, 以聚类形成的子集作为构图的节点, 从而大大降低了图的复杂度. 新的聚类方法计算量较小, 通过推导得到的距离函数能较好地保持原有数据分布. 实验结果表明, 通过局部聚类后构建的小图在分类效果上与在原图上的结果相当, 同时在计算速度上有极大的提高.     

A GRAPH‐BASED APPROACH FOR SEMISUPERVISED CLUSTERING

This paper proposes a graph‐based approach for semisupervised clustering based on pairwise relations among instances. In our approach, the entire data set is represented as an edge‐weighted graph by mapping each data element (instance) as a vertex and connecting the instances by edges with their similarities. In order to reflect pairwise constraints on the clustering process, the graph is modified by contraction as it is known from general graph theory and the graph Laplacian in spectral graph theory. The graph representation enables us to deal with pairwise constraints as well as pairwise similarities over the same unified representation. By exploiting the constraints as well as similarities among instances, the entire data set is projected onto a subspace via the modified graph, and data clustering is conducted over the projected representation. The proposed approach is evaluated over several real‐world data sets. The results are encouraging and show that it is worthwhile to pursue the proposed approach.     

Multiple Instance Learning via Semi-supervised Laplacian TSVM

Multiple instance learning attempts to learn from a training set consists of labeled bags each containing many unlabeled instances. In previous works, most existing algorithms mainly pay attention to the ‘most positive’ instance in each positive bag, but ignore the other instances. For utilizing these unlabeled instances in positive bags, we present a new multiple instance learning algorithm via semi-supervised laplacian twin support vector machines (called Miss-LTSVM). In Miss-LTSVM, all instances in positive bags are used in the manifold regularization terms for improving the performance of classifier. For verifying the effectiveness of the presented method, a series of comparative experiments are performed on seven multiple instance data sets. Experimental results show that the proposed method has better classification accuracy than other methods in most cases.     

Constraint Score Evaluation for Spectral Feature Selection

Semi-supervised context characterized by the presence of a few pairs of constraints between learning samples is abundant in many real applications. Analysing these instance constraints by recent spectral scores has shown good performances for semi-supervised feature selection. The performance evaluation of these scores is generally based on classification accuracy and is performed in a ground truth context. However, this supervised context used by the evaluation step is inconsistent with the semi-supervised context in which the feature selection operates. In this paper, we propose a semi-supervised performance evaluation procedure, so that both feature selection and clustering steps take into account the constraints given by the user. In this way, the selection and the evaluation steps are performed in the same context which is close to real life applications. Extensive experiments on benchmark datasets are carried out in the last section. These experiments are performed using a supervised classical evaluation and the semi-supervised proposed one. They demonstrate the effectiveness of feature selection based on constraint analysis that uses both pairwise constraints and the information brought by the unlabeled data.     

Semi-supervised hyperspectral image classification algorithm based on graph embedding and discriminative spatial information

Image classification is one of the important techniques in computer vision. Due to the limited access of labeled samples in hyperspectral images, semi-supervised learning (SSL) methods have been widely applied in hyperspectral image classification. Graph based semi-supervised learning provides an effective solution to model data in classification problems, of which graph construction is the critical step. In this paper we employ the graphs constructed with a typical manifold learning method-locally linear embedding (LLE), based on which semi-supervised classification is then conducted. To exploit the valuable spatial information contained in hyperspectral images, discriminative spatial information (DSI) is then extracted. The proposed classification method is evaluated using three real hyperspectral data sets, revealing state-of-art performance when compared with different classification methods.     

调整聚类假设联合成对约束半监督分类方法

当不同类别的样本严重重叠在分类边界时,由于聚类假设不能很好地反映出数据的真实分布,基于聚类假设的半监督分类方法的性能,可能比与之对立的监督分类方法更差。针对上述不安全的半监督分类问题,提出了调整聚类假设联合成对约束半监督分类方法（ACA-JPC-S3VM）。一方面,它将单个未标记样本到数据分布边界的距离融入到模型的学习中,能够一定程度上缓解此类情况下算法性能的下降程度;另一方面,它将成对约束信息引入,弥补了模型对监督信息利用方面的不足。在UCI数据集上的实验结果表明,ACA-JPC-S3VM方法的性能绝不会低于支持向量机（SVM）,且在标记样本数量为10时的平均准确率较SVM高出5个百分点;在图像分类数据集上的实验结果表明,直推式支持向量机（TSVM）等半监督分类方法出现了不同程度的不安全学习情形（即性能相近或低于SVM）,而ACA-JPC-S3VM却能安全地学习。因此,ACA-JPC-S3VM具有更好的安全性与正确性。     

基于成对约束的非线性维数约减框架

半监督维数约简是指借助于辅助信息与大量无标记样本信息从高维数据空间找到一个最优低维判别空间,便于后续的分类或聚类操作,它被看作是理解基因序列、文本与人脸图像等高维数据的有效方法。提出一个基于成对约束的半监督维数约简一般框架（SSPC）。该方法首先通过使用成对约束和无标号样本的内在几何结构学习一个判别邻接矩阵;其次,新方法应用学到的投影将原来高维空间中的数据映射到低维空间中,以至于聚类内的样本之间距离变得更加紧凑,而不同聚类间的样本之间距离变得尽可能得远。所提出的算法不仅能找到一个最佳的线性判别子空间,还可以揭示流形数据的非线性结构。在一些真实数据集上的实验结果表明,新方法的性能优于当前主流基于成对约束的维数约简算法的性能。     

基于近邻元分析的半监督流形学习算法*

现有的大多数流形学习算法偏重保持流形的几何结构,并未考虑到样本点的标签信息,这在一定程度上限制了流形学习算法在数据分类中的应用.因此文中提出一种基于近邻元分析的半监督流形学习算法,采用近邻元分析学习距离度量矩阵,在距离度量方式下选择样本点的局部邻域点.基于距离度量方式构造样本点和邻域点的局部几何结构,并在样本点的低维嵌入坐标中保持这种局部几何结构不变.3个不同数据集上的分类实验验证了文中算法的有效性.     

Glocalization pursuit support vector machine

Graph-based methods have aroused wide interest in pattern recognition and machine learning, which capture the structural information in data into classifier design through defining a graph over the data and assuming label smoothness over the graph. Laplacian Support Vector Machine (LapSVM) is a representative of these methods and an extension of the traditional SVM by optimizing a new objective additionally appended Laplacian regularizer. The regularizer utilizes the local linear patches to approximate the data manifold structure and assumes the same label of the data on each patch. Though LapSVM has shown more effective classification performance than SVM experimentally, it in fact concerns more the locality than the globality of data manifold due to the Laplacian regularizer itself. As a result, LapSVM is relatively sensitive to the local change of the data and cannot characterize the manifold quite faithfully. In this paper, we design an alternative regularizer, termed as Glocalization Pursuit Regularizer. The new regularizer introduces a natural global structure measure to grasp the global and local manifold information as simultaneously as possible, which can be proved to make the representation of the manifold more compact than the Laplacian regularizer. We further introduce the new regularizer into SVM to develop an alternative graph-based SVM, called as Glocalization Pursuit Support Vector Machine (GPSVM). GPSVM not only inherits the advantages of both SVM and LapSVM but also uses the structural information more reasonably to guide the classifier design. The experiments both on the toy and real-world datasets demonstrate the better classification performance of our proposed GPSVM compared with SVM and LapSVM.     

Mixture graph based semi-supervised dimensionality reduction

Graph structure is crucial to graph based dimensionality reduction. A mixture graph based semi-supervised dimensionality reduction (MGSSDR) method with pairwise constraints is proposed. MGSSDR first constructs multiple diverse graphs on different random subspaces of dataset, then it combines these graphs into a mixture graph and does dimensionality reduction on this mixture graph. MGSSDR can preserve the pairwise constraints and local structure of samples in the reduced subspace. Meanwhile, it is robust to noise and neighborhood size. Experimental results on facial images feature extraction demonstrate its effectiveness.     

基于加权样本和共识率的标记传播算法

标记传播是使用最广泛的半监督分类方法之一。基于共识率的标记传播算法(Consensus Rate-based Label Propagation,CRLP)通过汇总多个聚类方法以合并数据各种属性得到的共识率来构造图。然而,CRLP算法与大多数基于图的半监督分类方法一样,在图中将每个标记样本视为同等重要,它们主要通过优化图的结构来提高算法的性能。事实上,样本不一定是均匀分布的,不同的样本在算法中的重要性也是不同的,并且CRLP算法容易受聚类数目和聚类方法的影响,对低维数据的适应性不足。针对这些问题,文中提出了一种基于加权样本和共识率的标记传播算法(Label Propagation Algorithm Based on Weighted Samples and Consensus-Rate,WSCRLP)。WSCRLP算法首先对数据集进行多次聚类,以探索样本的结构,并结合共识率和样本的局部信息构造图;然后为不同分布的标记样本分配不同的权重;最后基于构造的图和加权样本进行半监督分类。在真实数据集上的实验表明,WSCRLP算法对标记样本进行加权和构造图的方法可以显著提高分类准确率,在84%的实验中都优于对比方法。相比CRLP算法,WSCRLP算法不仅具有更好的性能,而且对输入参数具有鲁棒性。     

基于成对约束的判别型半监督聚类分析

现有一些典型的半监督聚类方法一方面难以有效地解决成对约束的违反问题,另一方面未能同时处理高维数据.通过提出一种基于成对约束的判别型半监督聚类分析方法来同时解决上述问题.该方法有效地利用了监督信息集成数据降维和聚类,即在投影空间中使用基于成对约束的K均值算法对数据聚类,再利用聚类结果选择投影空间.同时,该算法降低了基于约束的半监督聚类算法的计算复杂度,并解决了聚类过程中成对约束的违反问题.在一组真实数据集上的实验结果表明,与现有相关半监督聚类算法相比,新方法不仅能够处理高维数据,还有效地提高了聚类性能.     

互信息与多条元路径融合的异质网络表示学习方法

异质信息网络能够对真实世界的诸多复杂应用场景进行建模,其表示学习研究也得到了众多学者的广泛关注.现有的异质网络表示学习方法大多基于元路径来捕获网络中的结构和语义信息,已经在后续的网络分析任务中取得很好的效果.然而,此类方法忽略了元路径的内部节点信息和不同元路径实例的重要性;仅能捕捉到节点的局部信息.因此,提出互信息与多条元路径融合的异质网络表示学习方法.首先,利用一种称为关系旋转编码的元路径内部编码方式,基于相邻节点和元路径上下文节点捕获异质信息网络的结构和语义信息,采用注意力机制来建模各元路径实例的重要性;然后,提出一种互信息最大化与多条元路径融合的无监督异质网络表示学习方法,使用互信息捕获全局信息以及全局信息和局部信息之间的联系.最后,在两个真实数据集上进行实验,并与当前主流的算法进行比较分析.结果表明,所提方法在节点分类和聚类任务上性能都有提升,甚至和一些半监督算法相比也表现出强劲性能.     

Perceptual relativity-based semi-supervised dimensionality reduction algorithm

As we all know, a well-designed graph tends to result in good performance for graph-based semi-supervised learning. Although most graph-based semi-supervised dimensionality reduction approaches perform very well on clean data sets, they usually cannot construct a faithful graph which plays an important role in getting a good performance, when performing on the high dimensional, sparse or noisy data. So this will generally lead to a dramatic performance degradation. To deal with these issues, this paper proposes a feasible strategy called relative semi-supervised dimensionality reduction (RSSDR) by utilizing the perceptual relativity to semi-supervised dimensionality reduction. In RSSDR, firstly, relative transformation will be performed over the training samples to build the relative space. It should be indicated that relative transformation improves the distinguishing ability among data points and diminishes the impact of noise on semi-supervised dimensionality reduction. Secondly, the edge weights of neighborhood graph will be determined through minimizing the local reconstruction error in the relative space such that it can preserve the global geometric structure as well as the local one of the data. Extensive experiments on face, UCI, gene expression, artificial and noisy data sets have been provided to validate the feasibility and effectiveness of the proposed algorithm with the promising results both in classification accuracy and robustness.     

Data heterogeneity consideration in semi-supervised learning

In class (cluster) formation process of machine learning techniques, data instances are usually assumed to have equal relevance. However, it is frequently not true. Such a situation is more typical in semi-supervised learning since we have to understand the data structure of both labeled and unlabeled data at the same time. In this paper, we investigate the organizational heterogeneity of data in semi-supervised learning using graph representation. This is because graph is a natural choice to characterize relationship between any pair of nodes or any pair of groups of nodes, consequently, strategical location of each node or each group of nodes can be determined by graph measures. Specifically, two issues are addressed: (1) We propose an adaptive graph construction method, we call AdaRadius, considering the heterogeneity of local interacting structure among nodes. As a result, it presents several interesting properties, namely adaptability to data density variations, low dependency on parameters setting, and reasonable computational cost, for both pool based and incremental data. (2) Moreover, we present heuristic criteria for selecting representative data samples to be labeled. Experimental study shows that selective labeling usually gets better classification results than random labeling. To our knowledge, it still lacks investigation on both issues up to now, therefore, our approach presents an important step toward the data heterogeneity characterization not only in semi-supervised learning, but also in general machine learning.     

MILD: Multiple-Instance Learning via Disambiguation

In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set are associated with bags rather than instances. A bag is labeled positive if at least one of its instances is positive; otherwise, the bag is labeled negative. Since a positive bag may contain some negative instances in addition to one or more positive instances, the true labels for the instances in a positive bag may or may not be the same as the corresponding bag label and, consequently, the instance labels are inherently ambiguous. In this paper, we propose a very efficient and robust MIL method, called Multiple-Instance Learning via Disambiguation (MILD), for general MIL problems. First, we propose a novel disambiguation method to identify the true positive instances in the positive bags. Second, we propose two feature representation schemes, one for instance-level classification and the other for bag-level classification, to convert the MIL problem into a standard single-instance learning (SIL) problem that can be solved by well-known SIL algorithms, such as support vector machine. Third, an inductive semi-supervised learning method is proposed for MIL. We evaluate our methods extensively on several challenging MIL applications to demonstrate their promising efficiency, robustness, and accuracy.     

融合Garbor小波和流形学习的人脸表情识别

通过Garbor小波提取人脸表情特征,为降低Garbor变换后向量维数和提取有效的鉴别特征,将手动选取特征点和监督局部线性嵌入（SLLE）结合起来,利用人脸表情图像数据本身的非线性流形结构信息和样本标签信息来调整点到点之间的距离,并形成距离矩阵,而后基于被调整的距离矩阵进行线性近邻重建来实现维数约简,提取低维鉴别特征用于人脸表情识别。结果表明该方法能更为有效地提取反映表情状态的特征,识别率优于传统的PCA算法,取得了较好的识别效果。最后实验分析了SLLE算法近邻数K和嵌入维数对识别率的影响,得到了SLLE算法的最优近邻数K和低维嵌入维数。