Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)

By Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk. 2006

588 citations recorded by the source corpus.

Subfields: Natural Language Processing, Statistical Learning Theory

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Citations in the corpus

Product Quantization for Nearest Neighbor Search (2011, methodology): “In this case, one of the most popular ANN algorithms is the Euclidean Locality-Sensitive Hashing (E2LSH) [7], [11], which provides theoretical guarantees on the search quality This work was partly realized as part of the Quaero Programme, funded by OSEO, French State agency for innovation.”
Practical and Optimal LSH for Angular Distance (2015, methodology): “Nearest neighbor search is a key algorithmic problem with applications in several fields including computer vision, information retrieval, and machine learning [4].”
Query-driven iterated neighborhood graph search for large scale indexing (2012, unknown): “P ′ ← Perturbation( R ∗ , q, T, history ) R ∗′ ← LocalNGSearch( P ′ , G, history )”
Semi-Supervised Hashing for Large-Scale Search (2012, background): “In fact, finding nearest neighbors is a fundamental step in many machine learning algorithms such as kernel density estimation, spectral clustering, manifold learning and semisupervised learning [2].”
Kernelized locality-sensitive hashing for scalable image search (2009, background): “To make large-scale search practical, vision researchers have recently explored approximate similarity search techniques , where a predictable loss in accuracy is sacrificed in order to allow fast queries even for high-dimensional inputs [25] [24] [12] [16] [4].”
Searching with quantization: approximate nearest neighbor search using short codes and distance estimators (2009, methodology): “In that case, one of the most popular ANN algorithm is the Euclidean Locality-Sensitive Hashing (E2LSH) [5, 9 ], which provides theoretical guarantees on the search quality with limited assumptions.”
Optimized Cartesian K-Means (2014, unknown): “C V ] 16 M ay 2 01 4 Index Terms—Clustering, Cartesian product, Nearest neighbor search ✦”
Approximate Nearest Neighbor Search by Residual Vector Quantization (2010, background): “, Exact Euclidean Locality-Sensitive Hashing (E2LSH) [7,8]) involve indexing structures to improve the performance.”
Manhattan hashing for large-scale image retrieval (2012, methodology): “Manhattan Hashing for Large-Scale Image Retrieval Weihao Kong Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai, China kongweihao@cs.sjtu.edu.cn Wu-Jun Li Shanghai Key Laboratory of Scalable Computing an”
Semi-supervised hashing for scalable image retrieval (2010, background): “For large-scale applications, to avoid excessive computational and memory costs, one would like to instead do an Approximate Nearest Neighbor (ANN) search with sublinear query complexity [12] .”
Machine learning in protein engineering (2018, unknown): “…al., 2018) and genome annotation (Yip et al., 2013; Jurtz et al., 2017; Yue & Wang, 2018), which have been reviewed elsewhere. k -nearest-neighbor (Shakhnarovich et al., 2006) methods make predictions on new data points by taking the majority (for classiﬁcation) or mean (for regression) labels…”
Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection (2009, methodology): “Keywords: Nearest Neighbors Graph, High Dimensional Data, Divide and Conquer, Lanczos Algorithm, Spectral Method”
Improving Bag-of-Features for Large Scale Image Search (2010, methodology): “Note that this method is different from the Euclidean version of LSH (E2LSH) [7, 8], which produces several hash keys per descriptor.”