Hash Kernels

By Javen Qinfeng Shi, James Petterson, Gideon Dror, J. Langford, Alex Smola, Alexander L. Strehl, S. V. N. Vishwanathan. 2009

89 citations recorded by the source corpus.

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Citations in the corpus

Distributed learning of deep neural network over multiple agents (2018, methodology): “Interesting techniques include distributed gradient optimization[7, 8], online learning with delayed updates[9] and hashing and simpliﬁcation of kernels[10].”
Optimal Rates for Random Fourier Features (2015, background): “…this bottleneck, numerous solutions have been proposed, such as low-rank matrix approximations [28, 9, 1], explicit feature maps designed for additive kernels [26, 14], hashing [22, 12], and random Fourier features (RFF) [16] constructed for shift-invariant kernels, the focus of the current paper.”
Feature hashing for large scale multitask learning (2009, methodology): “In this sense, the present paper continues where (Shi et al., 2009) falls short: we prove exponential tail bounds.”
Hash Kernels for Structured Data (2009, background): “Hash kernel on strings Shi et al. (2009) propose a hash kernel to deal with the issue of computational efficiency by a very simple algorithm: high-dimensional vectors are compressed by adding up all coordinates which have the same hash value one only needs to perform as many calculations as there…”
High-dimensional signature compression for large-scale image classification (2011, methodology): “They have shown excellent performance in the compression of text features [26, 33] and faces [25].”
Juxtapp: A Scalable System for Detecting Code Reuse among Android Applications (2012, methodology): “Feature hashing is a popular and powerful technique for reducing the dimensionality of the data being analyzed [26, 31].”
A sparse Johnson: Lindenstrauss transform (2010, background): “Lastly,we note that random projection using hashing has found practical applications in machine learning [21,29,33].”
Streaming First Story Detection with application to Twitter (2010, background): “Recent examples of problems set in the streaming model include stream-based machine translation (Levenberg and Osborne, 2009), approximating kernel matrices of data streams (Shi et al., 2009), and topic modelling on streaming document collections (Yao et al.”
Large Scale Distributed Deep Networks (2012, background): “In response, a great many authors have explored scaling up machine learning algorithms through parallelization and distribution [11, 12, 13, 14, 15, 16, 17].”
Real-time visual tracking using compressive sensing (2011, background): “the connection between hash kernels [14] and CS.”
Recent Advances of Large-Scale Linear Classification (2012, background): “Examples include random Fourier projection [77], random projections [78], [79], polynomial approximation [80], and hashing [81]– [84].”
Supervised semantic indexing (2009, methodology): “On the full dictionary size experiments in Table 3 we also compare Hash Kernels [29] with our Correlated Feature Hashing method described in Section 2.2.1.”