Multi-Label 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 Multi-Label Dimensionality Reduction write by Liang Sun. This book was released on 2016-04-19. Multi-Label Dimensionality Reduction available in PDF, EPUB and Kindle. Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks
Multi-label Dimensionality Reduction
Multi-label 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 Multi-label Dimensionality Reduction write by Liang Sun. This book was released on 2011. Multi-label Dimensionality Reduction available in PDF, EPUB and Kindle. Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
Multilabel Classification
Multilabel Classification - 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 Multilabel Classification write by Francisco Herrera. This book was released on 2016-08-09. Multilabel Classification available in PDF, EPUB and Kindle. This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them.• The importance of taking advantage of label correlations to improve the results.• The different approaches followed to face multi-label classification.• The preprocessing techniques applicable to multi-label datasets.• The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
Feature-aware Label Space Dimension Reduction for Multi-label Classification Problem
Feature-aware Label Space Dimension Reduction for Multi-label Classification Problem - 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 Feature-aware Label Space Dimension Reduction for Multi-label Classification Problem write by 陳耀男. This book was released on 2012. Feature-aware Label Space Dimension Reduction for Multi-label Classification Problem available in PDF, EPUB and Kindle.
Neural Information Processing
Neural Information Processing - 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 Neural Information Processing write by Long Cheng. This book was released on 2018-12-03. Neural Information Processing available in PDF, EPUB and Kindle. The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The third volume, LNCS 11303, is organized in topical sections on embedded learning, transfer learning, reinforcement learning, and other learning approaches.
