Average case complexity of multivariate integration for smooth functions
Journal of Complexity
The solution of multidimensional real Helmholtz equations on Sparse Grids
SIAM Journal on Scientific Computing
Intrinsic Dimensionality Estimation With Optimally Topology Preserving Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
GTM: the generative topographic mapping
Neural Computation
Learning and Design of Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new approach to dimensionality reduction: theory and algorithms
SIAM Journal on Applied Mathematics
A Unified Model for Probabilistic Principal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Another look at principal curves and surfaces
Journal of Multivariate Analysis
Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns
Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Regularized principal manifolds
The Journal of Machine Learning Research
Towards a Black Box Algorithm for Nonlinear Function Approximation over High-Dimensional Domains
SIAM Journal on Scientific Computing
Probabilistic principal surface classifier
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Principal curves with bounded turn
IEEE Transactions on Information Theory
Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids
Journal of Computational Physics
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In this paper, we deal with the construction of lower-dimensional manifolds from high-dimensional data which is an important task in data mining, machine learning and statistics. Here, we consider principal manifolds as the minimum of a regularized, non-linear empirical quantization error functional. For the discretization we use a sparse grid method in latent parameter space. This approach avoids, to some extent, the curse of dimension of conventional grids like in the GTM approach. The arising non-linear problem is solved by a descent method which resembles the expectation maximization algorithm. We present our sparse grid principal manifold approach, discuss its properties and report on the results of numerical experiments for one-, two- and three-dimensional model problems.