Dimension Reduction Of High-dimensional Data Research Articles

Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis

Nowadays, opinion texts are quickly published on websites and social networks by various users in the form of short texts and also in high volumes and various fields. Because these texts reflect the opinions of many users, their processing and analysis, such as clustering, can be very useful in a variety of applications including politics, industry, commerce, and economics. High dimensions of the text representation decrease efficiency of clustering, and an effective solution for this challenge is reducing dimensions of texts. Manifold learning is a powerful tool for nonlinear dimension reduction of high-dimensional data. Therefore, in this paper, for increasing efficiency of opinion texts clustering, by manifold learning, dimensions of the represented opinion texts are reduced based on sentiment and semantics, and their intrinsic dimensions are extracted. Then, the clustering algorithm is applied to dimension-reduced opinion texts. The proposed approach helps us to cluster opinion texts with simultaneous consideration of sentiment and semantics, which has received very little attention in the previous works. This type of clustering helps users of opinion texts to obtain more useful information from texts and also provides more accurate summaries in applications, such as the summarization of opinion texts. Experimental results on three datasets show better performance of the proposed approach on opinion texts in terms of important measures for evaluating clustering efficiency. An improvement of about 9% is observed in terms of accuracy on the third dataset and clustering based on sentiment and semantics.

Clustering adaptive canonical correlations for high-dimensional multi-modal data

Multi-modal canonical correlation analysis (MCCA) is an important joint dimension reduction method and has been widely applied to clustering tasks of multi-modal data. MCCA-based clustering is usually dimension reduction of high-dimensional data followed by clustering of low-dimensional data. However, the two-stage clustering is difficult to ensure the adaptability of dimension reduction and clustering, which will affect the final clustering performance. To solve the issue, we propose a novel clustering adaptive multi-modal canonical correlations (CAMCCs) method, which constructs a unified optimization model of multi-modal correlation learning and clustering. The method not only realizes discriminant learning of correlation projection directions under unsupervised cases, but also is able to directly obtain class labels of multi-modal data. Additionally, the method also realizes out-of-sample extension in class labels. Solutions of CAMCCs are optimized by an iterative way, and we analyze its convergence. Extensive experimental results on various datasets have demonstrated the effectiveness of the method.

Interpretable sparse SIR for functional data

This work focuses on the issue of variable selection in functional regression. Unlike most work in this framework, our approach does not select isolated points in the definition domain of the predictors, nor does it rely on the expansion of the predictors in a given functional basis. It provides an approach to select full intervals made of consecutive points. This feature improves the interpretability of the estimated coefficients and is desirable in the functional framework for which small shifts are frequent when comparing one predictor (curve) to another. Our method is described in a semiparametric framework based on Sliced Inverse Regression (SIR). SIR is an effective method for dimension reduction of high-dimensional data which computes a linear projection of the predictors in a low-dimensional space, without loss on regression information. We extend the approaches of variable selection developed for multidimensional SIR to select intervals rather than separated evaluation points in the definition domain of the functional predictors. Different and equivalent formulations of SIR are combined in a shrinkage approach with a group-LASSO-like penalty. Finally, a fully automated iterative procedure is also proposed to find the critical (interpretable) intervals. The approach is proved efficient on simulated and real data.

A novel high-dimensional data dimension reduction algorithm based on ameliorated supportive vector machine

Along with the rapid advancement of computer science and data engineering, the complexity and dimension of the data considered are bursting. With the increase of the data dimension, the performance of high dimensional index structure drops rapidly. And these high-dimensional data usually contains many redundant, its essence dimension often is much smaller than the original data, so the high-dimensional data processing question boils down to the related dimension reduction method to cut some less relevant data and reduce its dimension. This paper integrates the ameliorated supportive vector machine to propose the novel high-dimensional data dimension reduction algorithm. Our algorithm optimises the traditional methodologies from the three layers: 1) data cleaning and pre-processing steps are added to enhance the feasibility of the captured data; 2) SVM is modified to hold the feature of higher robustness; 3) application scenarios are built to test the performance. The experimental result proves that the proposed method outperforms compared with other state-of-theart algorithms theoretically and numerically.

A novel high-dimensional data dimension reduction algorithm based on ameliorated supportive vector machine

Along with the rapid advancement of computer science and data engineering, the complexity and dimension of the data considered are bursting. With the increase of the data dimension, the performance of high dimensional index structure drops rapidly. And these high-dimensional data usually contains many redundant, its essence dimension often is much smaller than the original data, so the high-dimensional data processing question boils down to the related dimension reduction method to cut some less relevant data and reduce its dimension. This paper integrates the ameliorated supportive vector machine to propose the novel high-dimensional data dimension reduction algorithm. Our algorithm optimises the traditional methodologies from the three layers: 1) data cleaning and pre-processing steps are added to enhance the feasibility of the captured data; 2) SVM is modified to hold the feature of higher robustness; 3) application scenarios are built to test the performance. The experimental result proves that the proposed method outperforms compared with other state-of-theart algorithms theoretically and numerically.

A Classification Algorithm for High-dimensional Data

With the advent of high-dimensional stored big data and streaming data, suddenly machine learning on a very large scale has become a critical need. Such machine learning should be extremely fast, should scale up easily with volume and dimension, should be able to learn from streaming data, should automatically perform dimension reduction for high-dimensional data, and should be deployable on hardware. Neural networks are well positioned to address these challenges of large scale machine learning. In this paper, we present a method that can effectively handle large scale, high-dimensional data. It is an online method that can be used for both streaming and large volumes of stored big data. It primarily uses Kohonen nets, although only a few selected neurons (nodes) from multiple Kohonen nets are actually retained in the end; we discard all Kohonen nets after training. We use Kohonen nets both for dimensionality reduction through feature selection and for building an ensemble of classifiers using single Kohonen neurons. The method is meant to exploit massive parallelism and should be easily deployable on hardware that implements Kohonen nets. Some initial computational results are presented.

3차원 얼굴 인식을 위한 PSO와 다중 포인트 특징 추출을 이용한 RBFNNs 패턴분류기 설계

In this paper, 3D face recognition system is designed by using polynomial based on RBFNNs. In case of 2D face recognition, the recognition performance reduced by the external environmental factors such as illumination and facial pose. In order to compensate for these shortcomings of 2D face recognition, 3D face recognition. In the preprocessing part, according to the change of each position angle the obtained 3D face image shapes are changed into front image shapes through pose compensation. the depth data of face image shape by using Multiple Point Signature is extracted. Overall face depth information is obtained by using two or more reference points. The direct use of the extracted data an high-dimensional data leads to the deterioration of learning speed as well as recognition performance. We exploit principle component analysis(PCA) algorithm to conduct the dimension reduction of high-dimensional data. Parameter optimization is carried out with the aid of PSO for effective training and recognition. The proposed pattern classifier is experimented with and evaluated by using dataset obtained in IC & CI Lab.

Simulation Research of Feature Extraction Method Based on Nonlinear Manifold Learning

This paper deals with the study of Locally Linear Embedding (LLE) and Hessian LLE nonlinear feature extraction for high dimensional data dimension reduction. LLE and Hessian LLE algorithm which reveals the characteristics of nonlinear manifold learning were analyzed. LLE and Hessian LLE algorithm simulation research was studied through different kinds of sample for dimensionality reduction. LLE and Hessian LLE algorithm’s classification performance was compared in accordance with MDS. The simulation experimental results show that LLE and Hessian LLE are very effective feature extraction method for nonlinear manifold learning.

Dimension Reduction and Clustering of High Dimensional Data using Auto-Associative Neural Networks

The task to capture and interpret information hidden inside high-dimensional data can be considered very complicated and challenging. Usually, dimension reduction technique may be considered as the first step to data analysis and exploration. The focus of this paper is on high-dimensional data dimension reduction using a supervised artificial neural networks technique known as Auto-Associative Neural Networks (AANN). The AANN can be considered as a powerful tool in data analysis and clustering with the ability to deal with linear and nonlinear correlation among variables. This technique is sometimes referred to as nonlinear principal component analysis (NLPCA), Encoding-Decoding networks, or bottleneck neural networks (BNN) due to its unique structure. It reduces high-dimensional data into low-dimensional data on its bottleneck layer which can later be used for data transmission, clustering and visualization. In this paper, a structurally flexible AANN is developed by using high level computer language, applied and studied on two case studies of Iris flowers and Italian olive oils datasets. The purpose of the work was to investigate the ability of AANN to reduce dimension of high-dimensional data on small (Iris) and large (Olive) datasets. The results have shown that AANN has been able to compress high-dimensional data into only one or two non-linear principal components at its bottleneck layer with the highest accuracy of 98.9% and 82.1% for both datasets respectively. AANN has also managed to perform accurately in both reducing dimension and clustering data by only using small portion of training dataset.

High-Dimensional Data Dimension Reduction Based on KECA

Kernel entropy component analysis (KECA) reveals the original data’s structure by kernel matrix. This structure is related to the Renyi entropy of the data. KECA maintains the invariance of the original data’s structure by keeping the data’s Renyi entropy unchanged. This paper described the original data by several components on the purpose of dimension reduction. Then the KECA was applied in celestial spectra reduction and was compared with Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) by experiments. Experimental results show that the KECA is a good method in high-dimensional data reduction.

Learning gradients on manifolds

A common belief in high-dimensional data analysis is that data are concentrated on a low-dimensional manifold. This motivates simultaneous dimension reduction and regression on manifolds. We provide an algorithm for learning gradients on manifolds for dimension reduction for high-dimensional data with few observations. We obtain generalization error bounds for the gradient estimates and show that the convergence rate depends on the intrinsic dimension of the manifold and not on the dimension of the ambient space. We illustrate the efficacy of this approach empirically on simulated and real data and compare the method to other dimension reduction procedures.

Removable sets and approximation of eigenvalues and eigenfunctions on combinatorial graphs

A new concept of a removable set of vertices on a combinatorial graph is introduced. It is shown that eigenfunctions of a combinatorial Laplace operator L on a graph G which correspond to small eigenvalues can be reconstructed as limits of the so-called variational splines. Spaces of such variational splines determined by uniqueness sets which are compliments of removable sets. It is important that in some cases this spaces have very small dimension and can be described explicitly. It is shown that small eigenvalues of L can be approximated by eigenvalues of certain matrices acting in spline spaces. The results have potential applications to various problems such as high-dimensional data dimension reduction, image processing, computer graphics, visualization and learning theory.