US Patent Application 18202455. MODELING DISJOINT MANIFOLDS simplified abstract
Contents
MODELING DISJOINT MANIFOLDS
Organization Name
Inventor(s)
Jesse Cole Cresswell of Toronto (CA)
Brendan Leigh Ross of Toronto (CA)
Anthony Lawrence Caterini of TORONTO (CA)
Gabriel Loaiza Ganem of TORONTO (CA)
Bradley Craig Anderson Brown of Oakville (CA)
MODELING DISJOINT MANIFOLDS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18202455 titled 'MODELING DISJOINT MANIFOLDS
Simplified Explanation
- The patent application describes a computer model that can account for data samples in a high-dimensional space as lying on different manifolds. - Instead of representing the entire data set as a single manifold, the model treats it as a union of manifolds. - The model groups data samples that are expected to belong to the same underlying manifold. - For generative models, a sub-model is trained for each group using their respective data samples. - Each sub-model can account for the manifold of its corresponding group. - The overall generative model includes information on how frequently to sample from each sub-model to accurately represent the entire data set. - The grouping of data samples can also be used to improve classification accuracy in multi-class classification models. - Group data samples are weighed based on the estimated latent dimensionality of the group.
Original Abstract Submitted
A computer model is trained to account for data samples in a high-dimensional space as lying on different manifolds, rather than a single manifold to represent the data set, accounting for the data set as a whole as a union of manifolds. Different data samples that may be expected to belong to the same underlying manifold are determined by grouping the data. For generative models, a generative model may be trained that includes a sub-model for each group trained on that group's data samples, such that each sub-model can account for the manifold of that group. The overall generative model includes information describing the frequency to sample from each sub-model to correctly represent the data set as a whole in sampling. Multi-class classification models may also use the grouping to improve classification accuracy by weighing group data samples according to the estimated latent dimensionality of the group.
