Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering

Keyphrases

• 1-factor 16%
• 2-rank 16%
• Affine Subspaces 16%
• Big Data 100%
• Bregman Divergence 16%
• Compact Set 16%
• Constant Size 100%
• Cost Function 16%
• Embarrassingly Parallel 16%
• K-means 100%
• K-vector 16%
• Parallel Streaming 16%
• Projective Clustering 100%
• Rank-one Approximation 16%
• Singular Vectors 16%
• Small Sets 16%
• Special Class 16%
• Squared Euclidean Distance 50%
• Streaming Algorithms 16%
• Sum of Squared Distance 16%
• Update Time 16%

Mathematics

• Affine Subspace 33%
• Clustering 100%
• Compact Set 33%
• Cost Function 33%
• Euclidean Distance 100%
• Matrix (Mathematics) 33%
• Polynomial 33%
• Principal Component Analysis 100%
• Projective 100%
• Right Singular Vector 33%
• Small Set 33%

Computer Science

• Affine Subspace 33%
• Approximation (Algorithm) 33%
• Big Data 100%
• k-means Clustering 66%
• Principle Component 33%
• Rank Approximation 33%
• Singular Vector 33%
• Squared Distance 33%
• Squared Euclidean Distance 100%