Subspace, Latent Structure and Feature Selection
Springer Berlin (Verlag)
9783540341376 (ISBN)
Invited Contributions.- Discrete Component Analysis.- Overview and Recent Advances in Partial Least Squares.- Random Projection, Margins, Kernels, and Feature-Selection.- Some Aspects of Latent Structure Analysis.- Feature Selection for Dimensionality Reduction.- Contributed Papers.- Auxiliary Variational Information Maximization for Dimensionality Reduction.- Constructing Visual Models with a Latent Space Approach.- Is Feature Selection Still Necessary?.- Class-Specific Subspace Discriminant Analysis for High-Dimensional Data.- Incorporating Constraints and Prior Knowledge into Factorization Algorithms - An Application to 3D Recovery.- A Simple Feature Extraction for High Dimensional Image Representations.- Identifying Feature Relevance Using a Random Forest.- Generalization Bounds for Subspace Selection and Hyperbolic PCA.- Less Biased Measurement of Feature Selection Benefits.
| Erscheint lt. Verlag | 16.5.2006 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science | Theoretical Computer Science and General Issues |
| Zusatzinfo | X, 209 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 322 g |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | 3D • algorithm • Algorithm analysis and problem complexity • Algorithmic Learning • algorithms • Bayesian inference • Calculus • Clustering • dimension reduction • Feature Selection • image reconstruction • latent structure analysis • learning • machine learning • optimisation methods • Optimization • Statistica • Statistical Analysis • Statistical Learning • statistical modeling |
| ISBN-13 | 9783540341376 / 9783540341376 |
| Zustand | Neuware |
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