e-journal

Graph Based Constrained Semi-Supervised Learning Framework via Label Propagation over Adaptive Neighborhood

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A new graph based constrained semi-supervised learning (G-CSSL) framework is proposed. Pairwise constraints (PC) are used to specify the types (intra- or inter-class) of points with labels. Since the number of labeled data is typically small in SSL setting, the core idea of this framework is to create and enrich the PC sets using the propagated soft labels from both labeled and unlabeled data by special label propagation (SLP), and hence obtaining more supervised information for delivering enhanced performance. We also propose a Two-stage Sparse Coding, termed TSC, for achieving adaptive neighborhood for SLP. The first stage aims at correcting the possible corruptions in data and training an informative dictionary, and the second stage focuses on sparse coding. To deliver enhanced inter-class separation and intra-class compactness, we also present a mixed soft-similarity measure to evaluate the similarity/dissimilarity of constrained pairs using the sparse codes and outputted probabilistic values by SLP. Simulations on the synthetic and real datasets demonstrated the validity of our algorithms for data representation and image recognition, compared with other related state-of-the-art graph based semi-supervised techniques.
Index Terms—Constrained semi-supervised learning, label propagation, adaptive neighborhood, sparse coding, soft-similarity measure, subspace learning

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2015

Deskripsi Fisik

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 27, NO. 9, SEPTEMBER 2015

Bahasa

English

ISBN/ISSN

1041-4347

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 27, NO. 9, SEPTEMBER 2015

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

Lampiran Berkas

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