manifold的好资源，入门可看，高引

介绍manifold流形优化的通俗易懂的一本入门教程。

假设数据是均匀采样于一个高维欧氏空间中的低维流形，流形学习就是从高维采样数据中恢复低维流形结构，即找到高维空间中的低维流形，并求出相应的嵌入映射，以实现维数约简或者数据可视化。它是从观测到的现象中去寻找事物的本质，找到产生数据的内在规律。

Sparse Manifold Clustering and Embedding(SMCE)算法的matlab实现。 Copyright @ Ehsan Elhamifar, 2012

小秋SLAM笔记

An Introduction to Differentiable Manifolds and Riemannian Geometry.djvu

作为一个研究领域，约束优化已经非常成熟，并且存在一些解决该领域一般问题的强大技术。 在本书中，考虑了一类特殊约束，称为几何约束，它表示优化问题的解决方案存在于多方面。 这是最近的研究领域，为更一般的约束优化方法提供了强有力的替代方案。 经典约束优化技术在嵌入空间中工作，该嵌入空间的尺寸可以比歧管的尺寸大得多。 因此，在歧管上工作的优化算法具有较低的复杂性并且通常还具有更好的数值特性（例如，参见保持诸如能量的不变量的数值积分方案）。 作者将此称为约束搜索空间中的无约束优化。

An Introduction to Manifolds, by Loring W. Tu.

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems. This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.

Manifold learning theory and applications清晰版(英文).pdf。 注：版权归作者所有，请购买正版。 亚马逊价格：686.90/RMB