Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Building connected neighborhood graphs for isometric data embedding
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Building k-edge-connected neighborhood graph for distance-based data projection
Pattern Recognition Letters
Building k-Connected Neighborhood Graphs for Isometric Data Embedding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of the optimal parameter value for the Isomap algorithm
Pattern Recognition Letters
Building Connected Neighborhood Graphs for Locally Linear Embedding
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
IEEE Transactions on Pattern Analysis and Machine Intelligence
A more topologically stable locally linear embedding algorithm based on R*-tree
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
An effective double-bounded tree-connected Isomap algorithm for microarray data classification
Pattern Recognition Letters
A comparative study of nonlinear manifold learning methods for cancer microarray data classification
Expert Systems with Applications: An International Journal
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Neighborhood selection plays an important role in manifold learning algorithm. This paper proposes an Adaptive Neighborhood Select algorithm based on Local Linearity(ANSLL). Given manifold dimensionality d as a priori knowledge, ANSLL algorithm constructs neighborhood based on two principles: 1. data points in the same neighborhood should approximately lie on a d -dimensional linear subspace; 2. each neighborhood should be as large as possible. And in ASNLL algorithm PCA technique is exploited to measure the linearity of finite data points set. Moreover, we present an improved method of constructing neighborhood graph, which can improve the accuracy of geodesic distance estimate for isometric embedding. Experiments on both synthetic data sets and real data sets show that ANSLL algorithm can adaptively construct neighborhood according to local curvature of data manifold and then improve the performance of most manifold algorithms, such as ISOMAP and LLE.