Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning

Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Rich Zemel
; Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):199-207, 2013.

Abstract

Neighborhood Components Analysis (NCA) is a popular method for learning a distance metric to be used within a k-nearest neighbors (kNN) classifier. A key assumption built into the model is that each point stochastically selects a single neighbor, which makes the model well-justified only for kNN with k=1. However, kNN classifiers with k>1 are more robust and usually preferred in practice. Here we present kNCA, which generalizes NCA by learning distance metrics that are appropriate for kNN with arbitrary k. The main technical contribution is showing how to efficiently compute and optimize the expected accuracy of a kNN classifier. We apply similar ideas in an unsupervised setting to yield kSNE and ktSNE, generalizations of Stochastic Neighbor Embedding (SNE, tSNE) that operate on neighborhoods of size k, which provide an axis of control over embeddings that allow for more homogeneous and interpretable regions. Empirically, we show that kNCA often improves classification accuracy over state of the art methods, produces qualitative differences in the embeddings as k is varied, and is more robust with respect to label noise.

Cite this Paper


BibTeX
@InProceedings{pmlr-v28-tarlow13, title = {Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning}, author = {Daniel Tarlow and Kevin Swersky and Laurent Charlin and Ilya Sutskever and Rich Zemel}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {199--207}, year = {2013}, editor = {Sanjoy Dasgupta and David McAllester}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/tarlow13.pdf}, url = {http://proceedings.mlr.press/v28/tarlow13.html}, abstract = {Neighborhood Components Analysis (NCA) is a popular method for learning a distance metric to be used within a k-nearest neighbors (kNN) classifier. A key assumption built into the model is that each point stochastically selects a single neighbor, which makes the model well-justified only for kNN with k=1. However, kNN classifiers with k>1 are more robust and usually preferred in practice. Here we present kNCA, which generalizes NCA by learning distance metrics that are appropriate for kNN with arbitrary k. The main technical contribution is showing how to efficiently compute and optimize the expected accuracy of a kNN classifier. We apply similar ideas in an unsupervised setting to yield kSNE and ktSNE, generalizations of Stochastic Neighbor Embedding (SNE, tSNE) that operate on neighborhoods of size k, which provide an axis of control over embeddings that allow for more homogeneous and interpretable regions. Empirically, we show that kNCA often improves classification accuracy over state of the art methods, produces qualitative differences in the embeddings as k is varied, and is more robust with respect to label noise. } }
Endnote
%0 Conference Paper %T Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning %A Daniel Tarlow %A Kevin Swersky %A Laurent Charlin %A Ilya Sutskever %A Rich Zemel %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-tarlow13 %I PMLR %J Proceedings of Machine Learning Research %P 199--207 %U http://proceedings.mlr.press %V 28 %N 3 %W PMLR %X Neighborhood Components Analysis (NCA) is a popular method for learning a distance metric to be used within a k-nearest neighbors (kNN) classifier. A key assumption built into the model is that each point stochastically selects a single neighbor, which makes the model well-justified only for kNN with k=1. However, kNN classifiers with k>1 are more robust and usually preferred in practice. Here we present kNCA, which generalizes NCA by learning distance metrics that are appropriate for kNN with arbitrary k. The main technical contribution is showing how to efficiently compute and optimize the expected accuracy of a kNN classifier. We apply similar ideas in an unsupervised setting to yield kSNE and ktSNE, generalizations of Stochastic Neighbor Embedding (SNE, tSNE) that operate on neighborhoods of size k, which provide an axis of control over embeddings that allow for more homogeneous and interpretable regions. Empirically, we show that kNCA often improves classification accuracy over state of the art methods, produces qualitative differences in the embeddings as k is varied, and is more robust with respect to label noise.
RIS
TY - CPAPER TI - Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning AU - Daniel Tarlow AU - Kevin Swersky AU - Laurent Charlin AU - Ilya Sutskever AU - Rich Zemel BT - Proceedings of the 30th International Conference on Machine Learning PY - 2013/02/13 DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-tarlow13 PB - PMLR SP - 199 DP - PMLR EP - 207 L1 - http://proceedings.mlr.press/v28/tarlow13.pdf UR - http://proceedings.mlr.press/v28/tarlow13.html AB - Neighborhood Components Analysis (NCA) is a popular method for learning a distance metric to be used within a k-nearest neighbors (kNN) classifier. A key assumption built into the model is that each point stochastically selects a single neighbor, which makes the model well-justified only for kNN with k=1. However, kNN classifiers with k>1 are more robust and usually preferred in practice. Here we present kNCA, which generalizes NCA by learning distance metrics that are appropriate for kNN with arbitrary k. The main technical contribution is showing how to efficiently compute and optimize the expected accuracy of a kNN classifier. We apply similar ideas in an unsupervised setting to yield kSNE and ktSNE, generalizations of Stochastic Neighbor Embedding (SNE, tSNE) that operate on neighborhoods of size k, which provide an axis of control over embeddings that allow for more homogeneous and interpretable regions. Empirically, we show that kNCA often improves classification accuracy over state of the art methods, produces qualitative differences in the embeddings as k is varied, and is more robust with respect to label noise. ER -
APA
Tarlow, D., Swersky, K., Charlin, L., Sutskever, I. & Zemel, R.. (2013). Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. Proceedings of the 30th International Conference on Machine Learning, in PMLR 28(3):199-207

Related Material