Searching for "Dimensionality reduction and generalization." – sorted by Relevance.
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Closed-Form Supervised Dimensionality Reduction with Generalized Linear Models (Technical Report
- Closed-Form Supervised Dimensionality Reduction with Generalized Linear Models Irina Rish, Genady
- Cited by 2 (0 self) – Add To MetaCart
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Linear Dimensionality Reduction for Multi-label Classification
- Linear Dimensionality Reduction for Multi-label Classification Shuiwang Ji Arizona State
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Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
- of work on dimensionality reduction in general, most existing approaches do not explicitly take
- Cited by 189 (7 self) – Add To MetaCart
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Sparse eigen methods by d.c. programming
- Conclusion Dimensionality Reduction Generalized Eigenvalue Problem Sparsity Principal Component Analysis
- Cited by 8 (3 self) – Add To MetaCart
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Probabilistic Principal Component Analysis
- (PCA) (Jolliffe 1986) is a well-established technique for dimensionality reduction, and a chapter
- Cited by 256 (5 self) – Add To MetaCart
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Clustering of Web Users Based on Access Patterns
- on dimensionality reduction. The generalized sessions have a dimensionality of 1,628 except for the 50k testing set
- Cited by 26 (0 self) – Add To MetaCart
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Supervised Exponential Family Principal Component Analysis via Convex Optimization
- dimensionality reduction principle of PCA is extended to the general exponential family [4, 7, 13]. Previous work
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Neighborhood preserving embedding
- for Dimensionality Reduction It is generally believed that the face space is a submanifold embedded in the ambient
- Cited by 12 (5 self) – Add To MetaCart
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Feature Selection for Web Page Classification
- requiring to download the linked pages. 4. Dimensionality reduction Generally the high dimensionality
- Cited by 10 (0 self) – Add To MetaCart
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Multilinear Subspace Analysis of Image Ensembles
- reduction over the multiple affiliated vector spaces. Multilinear dimensionality reduction generalizes
- Cited by 38 (0 self) – Add To MetaCart

