We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to the best published individual models. We present empirical results demonstrating signiﬁcantly better accuracy compared to the state-of-the-art achieved by either na¨ve Bayes ı or maximum entropy models, on Senseval-2 data. We also contrast against another type of kernel method, the support vector machine (SVM) model, and show that our KPCA-based model outperforms the SVM-based model. ...
This is the first book in a three-volume series deploying MATLAB-based applications in almost every branch of science. This volume, presents interesting topics from different areas of engineering, signal and image processing based on the MATLAB environment. The book consists of 20 excellent, insightful articles and the readers will find the results very useful to their work. This collection of high quality articles, refers to a large range of professional fields and may be used for scientific, engineering and educational purposes....
This chapter starts by reviewing some of the early research efforts in independent component analysis (ICA), especially the technique based on nonlinear decorrelation, that was successfully used by Jutten, H´ rault, and Ans to solve the ﬁrst ICA problems. e Today, this work is mainly of historical interest, because there exist several more efﬁcient algorithms for ICA. Nonlinear decorrelation can be seen as an extension of second-order methods such as whitening and principal component analysis (PCA)....
Các thành phần chủ yếu, nhân tố, và phân tích cụm, và ứng dụng trong phân tích khu vực xã hội
Chương này thảo luận về ba phương pháp phân tích đa biến quan trọng thống kê: thành phần chủ yếu phân tích (PCA), phân tích yếu tố (FA), và phân tích cluster (CA). PCA và FA thường được sử dụng với nhau để giảm dữ liệu bằng cách cơ cấu nhiều biến thành một số hạn chế của các thành phần (yếu tố).
ICA by Nonlinear Decorrelation and Nonlinear PCA
This chapter starts by reviewing some of the early research efforts in independent component analysis (ICA), especially the technique based on nonlinear decorrelation, that was successfully used by Jutten, H´ rault, and Ans to solve the ﬁrst ICA problems. e Today, this work is mainly of historical interest, because there exist several more efﬁcient algorithms for ICA. Nonlinear decorrelation can be seen as an extension of second-order methods such as whitening and principal component analysis (PCA).
Face recognition is still a vividly researched area in computer science. First attempts
were made in early 1970-ies, but a real boom happened around 1988, parallel with a large
increase in computational power. The first widely accepted algorithm of that time was the
PCA or eigenfaces method, which even today is used not only as a benchmark method to
compare new methods to, but as a base for many methods derived from the original idea.
The paper presents and discusses the methodology used and the results obtained by the application of the Principal Component Analysis (PCA) on a set of socio-economical and land use data collected in the Duy Tien district (Ha Nam province), Vietnam. Objective of this study is to use PCA as a data reduction method to verify if a relation could be established between the quantities of waste generated in a region and its land use and socio-economical characteristics.
Daruwalla et al. Journal of Orthopaedic Surgery and Research 2010, 5:21 http://www.josr-online.com/content/5/1/21
An application of principal component analysis to the clavicle and clavicle fixation devices
Zubin J Daruwalla1*, Patrick Courtis2, Clare Fitzpatrick2, David Fitzpatrick2, Hannan Mullett1
Background: Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed.
We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semisupervised metric learning algorithms to those generated by previously proposed unsupervised dimensionality reduction methods (e.g., PCA). Through a variety of experiments on different realworld datasets, we ﬁnd IDML-IT, a semisupervised metric learning algorithm to be the most effective.