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Embedding Learning

Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article, we introduce a novel framework of embedding learning to deliver a higher learning accuracy than the two-stage method while identifying an optimal learning-adaptive embedding. In particular, we propose a concept of U-minimal sufficient learning-adaptive embeddings, based on which we seek an optimal one to maximize the learning accuracy subject to an embedding constraint. Moreover, when specializing the general framework to classification, we derive a graph embedding classifier based on a hyperlink tensor representing multiple hypergraphs, directed or undirected, characterizing multi-way relations of unstructured data. Numerically, we design algorithms based on blockwise coordinate descent and projected gradient descent to implement linear and feed-forward neural network classifiers, respectively. Theoretically, we establish a learning theory to quantify the generalization error of the proposed method. Moreover, we show, in linear regression, that the one-hot encoder is more preferable among two-stage methods, yet its dimension restriction hinders its predictive performance. For a graph embedding classifier, the generalization error matches up to the standard fast rate or the parametric rate for linear or nonlinear classification. Finally, we demonstrate the utility of the classifiers on two benchmarks in grammatical classification and sentiment analysis. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION; Vol.117 No.537 March 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 307-319

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

TEXT MINING
BENTUK GALAKSI
AUTOMATIC FEATURE GENERATION
NATURAL LANGUAGE PROCESSING
REPRESENTATIONAL LEARNING
SENTIMENTAL ANALYSIS

Info Detail Spesifik

https://doi.org/10.1080/01621459.2020.1775614

Pernyataan Tanggungjawab

Ben Dai, Xiaotong Shen, Junhui Wang

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