Dimension Reduction Computational Complexity And Information

Dimension Reduction, Computational Complexity, and Information

Proceedings of the 23rd International Conference on Industrial Engineering and Engineering Management 2016

International Conference on Industrial Engineering and Engineering Management is sponsored by Chinese Industrial Engineering Institution, CMES, which is the unique national-level academic society of Industrial Engineering. The conference is held annually as the major event in this area. Being the largest and the most authoritative international academic conference held in China, it supplies an academic platform for the experts and the entrepreneurs in International Industrial Engineering and Management area to exchange their research results. Many experts in various fields from China and foreign countries gather together in the conference to review, exchange, summarize and promote their achievements in Industrial Engineering and Engineering Management fields. Some experts pay special attention to the current situation of the related techniques application in China as well as their future prospect, such as Industry 4.0, Green Product Design, Quality Control and Management, Supply Chain and logistics Management to cater for the purpose of low-carbon, energy-saving and emission-reduction and so on. They also come up with their assumption and outlook about the related techniques' development. The proceedings will offer theatrical methods and technique application cases for experts from college and university, research institution and enterprises who are engaged in theoretical research of Industrial Engineering and Engineering Management and its technique's application in China. As all the papers are feathered by higher level of academic and application value, they also provide research data for foreign scholars who occupy themselves in investigating the enterprises and engineering management of Chinese style.

Dimensionality Reduction in Machine Learning

Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data. - Provides readers with a comprehensive overview of various dimension reduction algorithms, including linear methods, non-linear methods, and deep learning methods - Covers the implementation aspects of algorithms supported by numerous code examples - Compares different algorithms so the reader can understand which algorithm is suitable for their purpose - Includes algorithm examples that are supported by a Github repository which consists of full notebooks for the programming code