Two Embedding Theorems for Data with Equivalences under Finite Group Action
Fabian Lim

TL;DR
This paper develops two embedding theorems for data spaces with group-based equivalences, enabling dimension reduction while respecting data symmetries, with applications in non-sequential data compression.
Contribution
It introduces analogues of Whitney and Johnson-Lindenstrauss theorems for data under finite group actions, incorporating invariants and two-step embeddings.
Findings
Embedding complexity depends on the size of the canonical data subset.
Invariant-based embeddings can effectively handle data equivalences.
Theorems extend classical embedding results to symmetry-aware data spaces.
Abstract
There is recent interest in compressing data sets for non-sequential settings, where lack of obvious orderings on their data space, require notions of data equivalences to be considered. For example, Varshney & Goyal (DCC, 2006) considered multiset equivalences, while Choi & Szpankowski (IEEE Trans. IT, 2012) considered isomorphic equivalences in graphs. Here equivalences are considered under a relatively broad framework - finite-dimensional, non-sequential data spaces with equivalences under group action, for which analogues of two well-studied embedding theorems are derived: the Whitney embedding theorem and the Johnson-Lindenstrauss lemma. Only the canonical data points need to be carefully embedded, each such point representing a set of data points equivalent under group action. Two-step embeddings are considered. First, a group invariant is applied to account for equivalences, and…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Tensor decomposition and applications · Statistical Methods and Inference
