Understand
We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space.
- The algorithm is appropriate when it is necessary to cluster data efficiently as it streams in, and is to be contrasted with traditional batch clustering algorithms that have access to all data at once.
- For example, Links has been successfully applied to embedding vectors generated from face images or voice recordings for the purpose of recognizing people, thereby providing real-time identification during video or audio capture.
Built on
“The hungarian method for the assignment problem,”
Harold W. Kuhn, · 1955
Earlier work this paper cites.
“Spherical averages and applications to spherical splines and interpolation,”
Samuel R. Buss and Jay P. Fillmore, · 2001
Earlier work this paper cites.
Cluster Analysis
Brian Everitt, · 2011
Earlier work this paper cites.
Similar
Handbook of Cluster Analysis
Christian Hennig, Marina Meila, Fionn Murtagh, and Roberto Rocci, · 2015
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“Small-variance nonparametric clustering on the hypersphere,”
Julian Straub, Trevor Campbell, Jonathan P. How, and John W. Fisher, · 2015
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“Facenet: A unified embedding for face recognition and clustering,”
F. Schroff, D. Kalenichenko, and J. Philbin, · 2015
Cited alongside, same era.
Then
“Generalized end-to-end loss for speaker verification,”
Li Wan, Quan Wang, Alan Papir, and Ignacio Lopez Moreno, · 2017
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“Speaker diarization with lstm,”
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, and Ignacio Lopez Moreno, · 2017
Later among the works it cites.
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