一种自适应邻域选择半监督判别分析算法

为克服边界Fisher判别分析(MFA)只利用少量有标记样本和构建邻域不能充分反映流形学习对邻域要求的缺点,提出一种基于局部线性结构的自适应邻域选择半监督判别分析的算法。采用自适应算法扩大或者缩小近邻系数k来构建邻域以保持局部线性结构。MFA通过少量有类别标签样本进行降维的同时UDP对大量无标签样本进行学习,以半监督的方法对高维人脸数据进行维数约减。最后,在ORL和YALE人脸数据库通过实验结果验证了该算法的有效性。     

一种邻域自适应半监督局部Fisher判别分析算法

针对利用局部化思想解决多模数据的判别分析问题时,根据经验对局部邻域大小进行全局统一设定无法体现局部几何结构的差异性的不足,提出一种邻域自适应半监督局部Fisher判别分析（neighborhood adaptive semi-supervised local Fisher discriminant analysis,NA-SELF）算法。该算法在半监督局部Fisher判别分析算法的基础上,结合马氏距离和余弦相似度确定初始近邻数,并根据样本空间概率密度估计调整近邻数。通过人工数据集和5组UCI标准数据集对该算法的特征降维性能进行验证,并与典型的维数约简算法和采用传统k近邻方法的判别分析算法进行比较,实验结果表明该算法具备更高的有效性。     

一种自适应邻域选择算法

提出一种自适应邻域选择算法,适用于所有基于局部的流形学习算法.该算法能够根据数据集分布的不同密度和曲率选择合适的邻域大小,同时结合局部多维尺度变换(LMDS),在合适的邻域下直接降维并通过全局整合得到数据集的低维坐标.实验表明该算法可较好恢复较复杂数据集的低维几何结构.     

自适应局部线性降维方法

高维数据降维方法已经被广泛应用在信息检索、模式识别、数据挖掘和人工智能等领域。针对目前流形学习方法的嵌入效果非常敏感于局部邻域的选取方式,提出一种自适应邻域选择的局部线性降维方法。该方法评估真实数据的固有维数,判断每一数据点的局部切方向,以便自适应地选择每一数据点的邻域数,使得不同数据集与邻域选取方式之间存在很好的自适应性,实现更好的降维效果。在人工生成数据集和医学数据上的仿真结果表明,该方法起到了良好的降维效果。     

基于自适应判别局部块对齐的人脸识别

针对有监督学习下的人脸识别问题,提出自适应判别局部块对齐SALDA(Self adaptive Local Discriminative Alignment)算法用于提取人脸特征。SALDA算法利用各样本点所具有的独特的局部近邻点分布,通过同类近邻点自动构造各样本点的局部邻域;基于已构造的局部邻域,SALDA提出一个自适应局部判别分析模型,所得到的局部判别信息通过全局排列转化为统一的全局特征表示。SALDA算法具有自适应构造局部邻域和自适应局部判别分析两个特点。通过在人脸数据库上的仿真实验,证明了所提出的SALDA算法在人脸识别上的有效性。     

流形学习中基于局部线性结构的自适应邻域选择

近年来,流形学习成为包括机器学习、模式识别和计算机视觉等相关领域的研究热点.流形学习算法中,邻域选择直接关系到算法的性能,而传统的邻域选择算法如k近邻和ε邻域算法存在参数难以确定,所构建邻域不能反映流形学习算法对邻域要求等缺点.提出了一种基于流形局部线性结构的自适应邻域选择算法(ANSLL).首先通过分析现有流形学习算法,总结出构建邻域的两个基本原则:1)同一邻域的所有点都近似地位于某一d维线性子空间内(d为流形维数);2)每个邻域包含尽可能多的点.基于这两个基本原则,ANSLL 算法采用主成分分析技术(PCA)度量有限点集的线性程度,通过邻域压缩或扩张方式自适应地构建邻域.针对邻域线性结构的特点,还提出了一种改进的邻域图构建方法,以提高等度映射(Isomap)算法中测地线距离估计的准确性.最后大量系统的实验表明,ANSLL算法能够依据流形的局部曲率自适应地构建邻域,从而提高大多数流形学习算法(如Isomap和LLE)的性能.     

基于自适应邻域选择的局部判别投影算法

在多模数据分类中,使用局部Fisher判别分析和边界Fisher分析方法构建邻域不能充分反映流形学习对邻域的要求.为此,提出一种基于自适应邻域选择的局部判别投影算法.采用自适应方法扩大或者缩小近邻系数k,以构建邻域,从而保持局部线性结构,揭示流形的内在几何结构,利用局部化方法使得投影空间中同类近邻样本尽量紧凑、异类近邻样本尽量分开.在ORL和YALE入脸数据库中进行实验,结果表明,在不同训练样本个数下,该算法均能获得较高的识别率.     

自适应子空间选择方法研究

由于维数灾难的原因,高维空间的数据聚类是一个具有挑战性的问题. 本文提出了一种自适应子空间选择的方法来解决这一难题．该方法采用局部线性嵌入的方法将高维数据映射到低维子空间上,然后采用两步迭代的方法自适应的选择最具有判别力的子空间：1）固定子空间不变,用 -均值聚类的方法产生类别的标号;2）固定类别的标号不变,用线性判别分析的方法将样本映射到低维子空间进行子空间选择. 通过反复迭代,样本在低维子空间进行有效聚类而避免了维数灾难,同时子空间自适应的调整到全局最优.大量的实验结果表明,该方法聚类效果优于传统的 -均值聚类．最后指出该方法的不足和进一步的研究方向．     

自适应邻域图的流形学习方法

针对目前流形学习方法的嵌入效果非常敏感于局部邻域的选取方式,提出一种自适应邻域图的非线性数据降维方法。该方法考虑数据点周围的点分布信息,自适应地寻找最近邻域大小。不同于传统的邻域选取方法,此方法根据样本点周围的疏密程度来动态地获得最近邻域数,且所得到的各个样本点的邻域数是不等的;将每个样本点与其最近邻点连接,构建自适应邻域图进行有效降维。在人工生成数据集和人脸数据上的仿真结果表明,提出的方法得到了良好的降维效果。     

边界流形嵌入在特征提取中的应用

样本点的边界信息对于分类具有重要意义。针对于边界Fisher分析（MFA）和局部敏感判别分析（LSDA）构造本征图和惩罚图所利用的样本点边界信息,在一些情况下并不能很好地表征不同类样本点的可分性,提出了一种新的图嵌入降维算法——边界流形嵌入（MME）。MME算法根据样本点的标签信息,寻找距离每个样本点最近的异类边界子流形,再返回本类中寻找距离异类边界子流形最近的同类边界子流形,从而定义出不同类样本间密切联系的同类边界邻域和异类边界邻域。通过最大化所有成对的边界子流形之间的距离,MME算法可以得到更具有鉴别意义的低维特征空间。同时,MME算法能将徘徊在边界的离群点收入到边界邻域里,这对减弱离群点给算法带来的负面的影响有一定的帮助。在人脸数据库上的实验结果表明了MME算法提取的低维特征能够提升分类的准确率。     

LDA/SVM driven nearest neighbor classification

Nearest neighbor (NN) classification relies on the assumption that class conditional probabilities are locally constant. This assumption becomes false in high dimensions with finite samples due to the curse of dimensionality. The NN rule introduces severe bias under these conditions. We propose a locally adaptive neighborhood morphing classification method to try to minimize bias. We use local support vector machine learning to estimate an effective metric for producing neighborhoods that are elongated along less discriminant feature dimensions and constricted along most discriminant ones. As a result, the class conditional probabilities can be expected to be approximately constant in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other competing techniques using a number of datasets.     

融合特征排序的多标记特征选择算法

在多标记学习框架中,特征选择是解决维数灾难,提高多标记分类器的有效手段。提出了一种融合特征排序的多标记特征选择算法。该算法首先在各标记下进行自适应的粒化样本,以此来构造特征与类别标记之间的邻域互信息。其次,对得到邻域互信息进行排序,使得每个类别标记下均能得到一组特征排序。最后,多个独立的特征排序经过聚类融合成一组新的特征排序。在4个多标记数据集和4个评价指标上的实验结果表明,所提算法优于一些当前流行的多标记降维方法。     

基于进化深度学习的特征提取算法

信息全面与维数灾难的矛盾是大数据时代网络态势感知需要解决的首要难题。特征提取一直是主流的降维方法,但现有算法对高维非线性数据效果不佳;深度学习是一类具有多层非线性映射的学习算法,可以完成复杂函数的逼近,但对隐层相关参数十分敏感。针对上述问题,将进化算法的思想引入深度学习,提出了一种基于进化深度学习的特征提取算法。该算法利用遗传算法及进化策略实现全局搜索及优化的特点,并对深度学习结构及相关参数进行了优化。理论分析及实验结果都证明了该算法的有效性。     

交互协调强化学习下的城市交通信号配时决策

针对应用传统强化学习进行城市自适应交通信号配时决策时存在维数灾难和缺乏协调机制等问题,提出引入交互协调机制的强化学习算法。以车均延误为性能指标设计了针对城市交通信号配时决策的独立Q-强化学习算法。在此基础上,通过引入直接交互机制对独立强化学习算法进行了延伸,即相邻交叉口交通信号控制agent间直接交换配时动作和交互点值。通过仿真实验分析表明,引入交互协调机制的强化学习的控制效果明显优于独立强化学习算法,协调更有效,并且其学习算法具有较好的收敛性能,交互点值趋向稳定。     

基于随机子空间的局部鉴别投影*

针对高维数据容易对噪声敏感及容易造成维数灾难问题,文中提出基于随机子空间的局部鉴别投影算法(RSLDP).利用随机子空间方法对高维的原始数据进行特征选择,在生成的低维特征子空间构造近邻图,降低噪声影响.RSLDP通过最大化局部类间加权散度和最小化局部类内加权散度,同时最小化样本的总体局部散度,改进局部最大间距鉴别嵌入算法,较好刻画样本与其类间类内近邻中心点的关系,有利于鉴别特征的提取.在CMU PIE和AR这2个人脸数据库上的实验表明文中算法的有效性.     

Dimensionality reduction-based spoken emotion recognition

To improve effectively the performance on spoken emotion recognition, it is needed to perform nonlinear dimensionality reduction for speech data lying on a nonlinear manifold embedded in a high-dimensional acoustic space. In this paper, a new supervised manifold learning algorithm for nonlinear dimensionality reduction, called modified supervised locally linear embedding algorithm (MSLLE) is proposed for spoken emotion recognition. MSLLE aims at enlarging the interclass distance while shrinking the intraclass distance in an effort to promote the discriminating power and generalization ability of low-dimensional embedded data representations. To compare the performance of MSLLE, not only three unsupervised dimensionality reduction methods, i.e., principal component analysis (PCA), locally linear embedding (LLE) and isometric mapping (Isomap), but also five supervised dimensionality reduction methods, i.e., linear discriminant analysis (LDA), supervised locally linear embedding (SLLE), local Fisher discriminant analysis (LFDA), neighborhood component analysis (NCA) and maximally collapsing metric learning (MCML), are used to perform dimensionality reduction on spoken emotion recognition tasks. Experimental results on two emotional speech databases, i.e. the spontaneous Chinese database and the acted Berlin database, confirm the validity and promising performance of the proposed method.     

Local sparse representation projections for face recognition

How to define the sparse affinity weight matrices is still an open problem in existing manifold learning algorithm. In this paper, we propose a novel supervised learning method called local sparse representation projections (LSRP) for linear dimensionality reduction. Differing from sparsity preserving projections (SPP) and the recent manifold learning methods such as locality preserving projections (LPP), LSRP introduces the local sparse representation information into the objective function. Although there are no labels used in the local sparse representation, it still can provide better measure coefficients and significant discriminant abilities. By combining the local interclass neighborhood relationships and sparse representation information, LSRP aims to preserve the local sparse reconstructive relationships of the data and simultaneously maximize the interclass separability. Comprehensive comparison and extensive experiments show that LSRP achieves higher recognition rates than principle component analysis, linear discriminant analysis and the state-of-the-art techniques such as LPP, SPP and maximum variance projections.     

Partially supervised speaker clustering

Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical model-based distance metrics, 2) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric, 3) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines, and 4) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance.