all AI news
Spatial Transformer K-Means. (arXiv:2202.07829v1 [cs.LG])
Feb. 17, 2022, 8:11 a.m. | Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan Sengupta, Richard Baraniuk, Behnaam Aazhang
cs.LG updates on arXiv.org arxiv.org
K-means defines one of the most employed centroid-based clustering algorithms
with performances tied to the data's embedding. Intricate data embeddings have
been designed to push $K$-means performances at the cost of reduced theoretical
guarantees and interpretability of the results. Instead, we propose preserving
the intrinsic data space and augment K-means with a similarity measure
invariant to non-rigid transformations. This enables (i) the reduction of
intrinsic nuisances associated with the data, reducing the complexity of the
clustering task and increasing performances …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Director, Global Procurement Data Analytics
@ Alcon | Fort Worth - Main
Backend Software Engineer, Airbnb for Real Estate
@ Airbnb | United States
Data Scientist
@ Exoticca | Barcelona, Catalonia, Spain - Remote
ESG Data Analytics Summer Associate (Intern)
@ Apex Clean Energy | Charlottesville, VA, United States
Team Lead, Machine Learning
@ Prenuvo | Vancouver, British Columbia, Canada