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Unsupervised Heterogeneous Coupling Learning for Categorical Representation

Complex categorical data is often hierarchically coupled with heterogeneous relationships between attributes and attribute values and the couplings between objects. Such value-to-object couplings are heterogeneous with complementary and inconsistent interactions and distributions. Limited research exists on unlabeled categorical data representations, ignores the heterogeneous and hierarchical couplings, underestimates data characteristics and complexities, and overuses redundant information, etc. The deep representation learning of unlabeled categorical data is challenging, overseeing such value-to-object couplings, complementarity and inconsistency, and requiring large data, disentanglement, and high computational power. This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings. UNTIE is efficiently optimized w.r.t. a kernel k -means objective function for unsupervised representation learning of heterogeneous and hierarchical value-to-object couplings. Theoretical analysis shows that UNTIE can represent categorical data with maximal separability while effectively represent heterogeneous couplings and disclose their roles in categorical data. The UNTIE-learned representations make significant performance improvement against the state-of-the-art categorical representations and deep representation models on 25 categorical data sets with diversified characteristics.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.1 January 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 533-549

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CATEGORICAL DATA
UNSUPERVISED LEARNING
REPRESENTATION LEARNING
COUPLING LEARNING
HETEROGENEITY LEARNING
NON-IID LEARNING
SIMILARITY LEARNING
CATEGORICAL DATA REPRESENTATION
UNSUPERVISED CATEGORICAL REPRESENTATION

Info Detail Spesifik

DOI: 10.1109/TPAMI.2020.3010953

Pernyataan Tanggungjawab

Chengzhang Zhu, Longbing Cao, Jianping Yin

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