Experiments with Random Projection

01/16/2013

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Recent theoretical work has identified random projection as a promising dimensionality reduction technique for learning mixtures of Gausians. Here we summarize these results and illustrate them by a wide variety of experiments on synthetic and real data.

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Experiments with Random Projection

Sparse Matrix-based Random Projection for Classification

Dimensionality Reduction with Subspace Structure Preservation

Stable Sparse Subspace Embedding for Dimensionality Reduction

Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching

Random Projections for k-means Clustering

DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality

Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalence

Experiments with Random Projection

Related Research

Sparse Matrix-based Random Projection for Classification

Dimensionality Reduction with Subspace Structure Preservation

Stable Sparse Subspace Embedding for Dimensionality Reduction

Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching

Random Projections for k-means Clustering

DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality

Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalence