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Heterogeneous Multi-task Semantic Feature Learning for Classification

Published: 17 October 2015 Publication History

Abstract

Multi-task Learning (MTL) aims to learn multiple related tasks simultaneously instead of separately to improve generalization performance of each task. Most existing MTL methods assumed that the multiple tasks to be learned have the same feature representation. However, this assumption may not hold for many real-world applications. In this paper, we study the problem of MTL with heterogeneous features for each task. To address this problem, we first construct an integrated graph of a set of bipartite graphs to build a connection among different tasks. We then propose a multi-task nonnegative matrix factorization (MTNMF) method to learn a common semantic feature space underlying different heterogeneous feature spaces of each task. Finally, based on the common semantic features and original heterogeneous features, we model the heterogenous MTL problem as a multi-task multi-view learning (MTMVL) problem. In this way, a number of existing MTMVL methods can be applied to solve the problem effectively. Extensive experiments on three real-world problems demonstrate the effectiveness of our proposed method.

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  • (2020)Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and RetrievalProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413574(1837-1845)Online publication date: 12-Oct-2020
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    cover image ACM Conferences
    CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
    October 2015
    1998 pages
    ISBN:9781450337946
    DOI:10.1145/2806416
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 17 October 2015

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    Author Tags

    1. heterogeneous features
    2. multi-task learning
    3. nonnegative matrix fatorization

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    CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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    Cited By

    View all
    • (2024)Transfer metric learning: algorithms, applications and outlooksVicinagearth10.1007/s44336-024-00003-81:1Online publication date: 25-Jun-2024
    • (2021)Multi-Relational Graph based Heterogeneous Multi-Task Learning in Community Question AnsweringProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482279(1038-1047)Online publication date: 26-Oct-2021
    • (2020)Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and RetrievalProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413574(1837-1845)Online publication date: 12-Oct-2020
    • (2019)Learning Wellness Profiles of Users on Social Networks: The Case of DiabetesSocial Web and Health Research10.1007/978-3-030-14714-3_8(139-169)Online publication date: 29-Jun-2019
    • (2018)Learning Perceptual Embeddings with Two Related Tasks for Joint Predictions of Media Interestingness and EmotionsProceedings of the 2018 ACM on International Conference on Multimedia Retrieval10.1145/3206025.3206071(420-427)Online publication date: 5-Jun-2018
    • (2018)Semantic Feature Learning for Heterogeneous Multitask Classification via Non-Negative Matrix FactorizationIEEE Transactions on Cybernetics10.1109/TCYB.2017.273281848:8(2284-2293)Online publication date: Aug-2018
    • (2018)Robust finite mixture regression for heterogeneous targetsData Mining and Knowledge Discovery10.1007/s10618-018-0564-z32:6(1509-1560)Online publication date: 1-Nov-2018
    • (2017)Exploiting high-order information in heterogeneous multi-task feature learningProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172228(2443-2449)Online publication date: 19-Aug-2017
    • (2017)Wellness Representation of Users in Social Media: Towards Joint Modelling of Heterogeneity and TemporalityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.272241129:10(2360-2373)Online publication date: 1-Oct-2017
    • (2017)Learning from semantically dependent multi-tasks2017 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2017.7966296(3498-3505)Online publication date: May-2017
    • Show More Cited By

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