基于核局部线性嵌入的基因表达谱数据分类

针对局部线性嵌入算法(Local Linear Embedding,LLE)利用试凑法寻找近邻数耗时的缺陷性,提出一种增强的核局部线性嵌入算法(Enhanced Kernel Local Linear Embedding,EKLLE)自动为样本分配邻域;该算法以高斯核函数为核心改进标准LLE距离度量准则,结合样本的类别信息,无需人工干预自动为样本设置不同的近邻数,克服了试凑法获得最优结果时需要大量时间;最后在各样本近邻数不相同的情况下对数据进行维数简约及待测样本分类。EKLLE算法有效地将高维基因表达谱数据映射到低维本质空间中,解决了传统LLE算法不能很好地处理含噪声或者稀疏数据的缺点。通过对比其他肿瘤样本分类实验,验证本文方法的实时性和精确性。

高光谱影像空-谱协同嵌入的地物分类算法

针对传统高光谱影像地物分类算法大多仅考虑光谱信息而忽略空间邻近像元间相关性的问题,提出了一种空-谱协同嵌入(SSCE)降维算法和空-谱协同最近邻(SSCNN)分类器。首先,定义一种空-谱协同距离,并将其应用于近邻选取和低维嵌入;然后,构建空-谱近邻关系图来保持数据中的流形结构,并在权值设置中增大空间近邻点的权重以增强数据间的聚集性,提取鉴别特征;最后使用SSCNN分类器对降维后的数据进行分类。利用PaviaU和Salinas高光谱数据集进行试验验证,结果表明,与传统的光谱分类算法相比,该算法能有效提高高光谱影像的地物分类精度。

基于LLE-k均值方法的中文文本聚类

文本聚类中,文本特征向量的高维特性使得对样本统计特征的评估十分困难,所以有必要进行有效的维数简约。LLE算法利用线性重构的局部对称性找出高维数据空间中的非线性结构,并在保持各数据点临近位置关系情况下,把高维空间数据点映射为低维空间对应的数据点。文章采用LLE-k均值方法进行中文文本聚类研究。首先利用LLE进行降维处理,然后对得到的线性特征向量用k均值进行聚类分析,与PCAI、SOMAP和LLE算法比较,结果显示LLE-k均值算法能得到更好的可视化效果。

Speech emotion recognition using semi-supervised discriminant analysis

Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.

SELF-DEPENDENT LOCALITY PRESERVING PROJECTION WITH TRANSFORMED SPACE-ORIENTED NEIGHBORHOOD GRAPH

Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.

General moving objects recognition method based on graph embedding dimension reduction algorithm

Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.