Robustness in Dimensionality Reduction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Robustness in Dimensionality Reduction write by Jiaxi Liang. This book was released on 2016. Robustness in Dimensionality Reduction available in PDF, EPUB and Kindle. Dimensionality reduction is widely used in many statistical applications, such as image analysis, microarray analysis, or text mining. This thesis focuses on three problems that relate to the robustness in dimension reduction. The first topic is the performance analysis in dimension reduction, that is, quantitatively assessing the performance of a algorithm on a given dataset. A criterion for success is established from the geometric point of view to address this issues. A family of goodness measures, called \textsl{local rank correlation}, is developed to assess the performance of dimensionality reduction methods. The potential application of the local rank correlation in selecting tuning parameters of dimension reduction algorithms is also explored. The second topic is the sensitivity analysis in dimension reduction. Two types of influence functions are developed as measures of robustness, based on which we develop graphical display strategies for visualizing the robustness of a dimension reduction method, and flagging potential outliers. In the third part of the thesis, a novel robust PCA framework, called \textsl{Performance-Weighted Bagging PCA}, is proposed from the perspective of model averaging. It obtains a robust linear subspace by weighted averaging a collection of subspaces produced by subsamples. The robustness against outliers is achieved by a proper weighting scheme, and possible choices of weighting scheme are investigated.
Dimensionality Reduction and Robustness Analysis of Large Scale Systems
Dimensionality Reduction and Robustness Analysis of Large Scale Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dimensionality Reduction and Robustness Analysis of Large Scale Systems write by Rudiyanto Gunawan. This book was released on 2000. Dimensionality Reduction and Robustness Analysis of Large Scale Systems available in PDF, EPUB and Kindle.
Robust Methods for Data Reduction
Robust Methods for Data Reduction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Robust Methods for Data Reduction write by Alessio Farcomeni. This book was released on 2016-01-13. Robust Methods for Data Reduction available in PDF, EPUB and Kindle. Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, dou
Data-Driven Science and Engineering
Data-Driven Science and Engineering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-Driven Science and Engineering write by Steven L. Brunton. This book was released on 2022-05-05. Data-Driven Science and Engineering available in PDF, EPUB and Kindle. A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Data Analytics in Bioinformatics
Data Analytics in Bioinformatics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Analytics in Bioinformatics write by Rabinarayan Satpathy. This book was released on 2021-01-20. Data Analytics in Bioinformatics available in PDF, EPUB and Kindle. Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
