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

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

图像视觉显著性检测算法在分析图像内容的过程中,引入了视觉特性,大大提高了 计算机对图像内容的理解程度,推进了图像处理技术的发展。在各种图像处理技术中, 计算机所关注的并不是图像的全部内容,而是一部分感兴趣区域或者非感兴趣区域。通 过视觉显著性检测算法去检测图像的显著区域,并给予不同区域不同的处理优先级,从 而更好的利用计算资源,提高计算效率。

程序中包含了多种流形学习算法:LLE、Isomap 、Laplacian、HLLE等及实验所用的非线性数据，如Swiss-Roll等；同时程序还为用户提供了界面，可以设置各种参数。程序中也包含了多种经典的模式识别算法，如MDS、PCA。

curvelet域流形学习人脸识别算法