Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced supervised locally linear embedding
Pattern Recognition Letters
Automatic Choice of the Number of Nearest Neighbors in Locally Linear Embedding
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image synthesis based on manifold learning
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Global and local choice of the number of nearest neighbors in locally linear embedding
Pattern Recognition Letters
Dimensionality reduction-based spoken emotion recognition
Multimedia Tools and Applications
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Locally linear embedding (LLE) is a recent unsupervised learning algorithm for non-linear dimensionality reduction of high dimensional data. One advantage of this algorithm is that just two parameters are needed to be set by user: the number of nearest neighbors and a regularization parameter. The choice of the regularization parameter plays an important role in the embedding results. In this paper, an automated method for choosing this parameter is proposed. Besides, in order to objectively qualify the performance of the embedding results, a new measure of embedding quality is suggested. Our approach is experimentally verified on 9 artificial data sets and 2 real world data sets. Numerical results are compared against two methods previously found in the state of art.