IEEE Trans Pattern Anal Mach Intell - Automatic Generation of Co-Embeddings from Relational Data with Adaptive Shaping.

Tópicos

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Resumo

In this paper, we study the co-embedding problem of how to map different types of patterns into one common low-dimensional space, given only the associations (relation values) between samples. We conduct a generic analysis to discover the commonalities between existing co-embedding algorithms and indirectly related approaches, and investigate possible factors controlling the shapes and distributions of the co-embeddings. The primary contribution of this work is a novel method for computing co-embeddings, termed the automatic co-embedding with adaptive shaping (ACAS) algorithm, based on an efficient transformation of the co-embedding problem. Its advantages include flexible model adaptation to the given data, an economical set of model variables leading to a parametric co-embedding formulation, and a robust model fitting criterion for model optimization based on a quantization procedure. The secondary contribution of this work is the introduction of a set of generic schemes for the qualitative analysis and quantitative assessment of the output of co-embedding algorithms, using existing labelled benchmark datasets. Experiments with synthetic and real-world datasets show that the proposed algorithm is very competitive compared to existing ones.

Resumo Limpo

paper studi coembed problem map differ type pattern one common lowdimension space given associ relat valu sampl conduct generic analysi discov common exist coembed algorithm indirect relat approach investig possibl factor control shape distribut coembed primari contribut work novel method comput coembed term automat coembed adapt shape aca algorithm base effici transform coembed problem advantag includ flexibl model adapt given data econom set model variabl lead parametr coembed formul robust model fit criterion model optim base quantiz procedur secondari contribut work introduct set generic scheme qualit analysi quantit assess output coembed algorithm use exist label benchmark dataset experi synthet realworld dataset show propos algorithm competit compar exist one

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