arXiv:2602.05869v2 Announce Type: replace-cross Abstract: We introduce Wedge Sampling, a new non-adaptive sampling scheme for low-rank tensor completion. We study recovery of an order-$k$ low-rank tensor of dimension $n \times \cdots \times n$ from a subset of its entries. Unlike the standard uniform entry model (i.e., i.i.d. samples from $[n]^k$), wedge sampling allocates observations to structured length-two patterns (wedges) in an associated bipartite sampling graph. By directly promoting these length-two connections, the sampling design strengthens the spectral signal that underlies effici
This is a new academic publication in the field of machine learning, representing incremental research progress.
For a sophisticated reader, this represents a technical advancement in a niche area of tensor completion, not a major market or geopolitical event.
This research potentially offers a more efficient method for low-rank tensor completion, which could improve certain data recovery and machine learning applications.
Improved efficiency in specific machine learning algorithms for data recovery.
Potentially enables more robust analysis of incomplete or sparse high-dimensional datasets.
Could indirectly lead to more capable AI models in data-intensive fields if widely adopted and scaled.
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Read at arXiv cs.LG