Turning Big Data Into Tiny Data: Coresets for Unsupervised Learning Problems

Keyphrases

• Big Data 100%
• Reduced Set 100%
• Learning Problems 100%
• Unsupervised Learning 100%
• Tiny Data 100%
• K-means 42%
• Cardinality 28%
• Principal Coordinate Analysis (PCoA) 14%
• Subspace Approximation 14%
• Large Set 14%
• High Dimensional Euclidean Space 14%
• Data Analysis Methods 14%
• Streaming Algorithms 14%
• Conceptual Contribution 14%
• Data Analysis Tasks 14%
• Weighted Point 14%
• Principal Subspace 14%
• Distributed Algorithms 14%
• Additive Constant 14%
• Small Sets 14%
• Input Space 14%
• Subspace Clustering 14%
• Low-dimensional Subspace 14%
• Database Management Systems 14%

Mathematics

• Original Set 100%
• Big Data 100%
• Cardinality 66%
• Principal Components 66%
• Wide Range 33%
• Dimensional Euclidean Space 33%
• Additive Constant 33%
• Data Point 33%
• Small Set 33%
• Set Point 33%
• Analysis Technique 33%
• Dimensional Subspace 33%
• Principal Component Analysis 33%