Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
A vector space model for automatic indexing
Communications of the ACM
Database-friendly random projections
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
An Algorithmic Theory of Learning: Robust Concepts and Random Projection
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Randomized algorithms and NLP: using locality sensitive hash function for high speed noun clustering
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Locality sensitive hash functions based on concomitant rank order statistics
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this work, we introduce a powerful and general feature representation based on a locality sensitive hash scheme called random hyperplane hashing. We are addressing the problem of centrally learning (linear) classification models from data that is distributed on a number of clients, and subsequently deploying these models on the same clients. Our main goal is to balance the accuracy of individual classifiers and different kinds of costs related to their deployment, including communication costs and computational complexity. We hence systematically study how well schemes for sparse high-dimensional data adapt to the much denser representations gained by random hyperplane hashing, how much data has to be transmitted to preserve enough of the semantics of each document, and how the representations affect the overall computational complexity. This paper provides theoretical results in the form of error bounds and margin based bounds to analyze the performance of classifiers learnt over the hash-based representation. We also present empirical evidence to illustrate the attractive properties of random hyperplane hashing over the conventional baseline representation of bag of words with and without feature selection.