Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is of particular importance in predicting multivariate or structured outputs which is an area of growing interest. In this paper we introduce a novel projection framework which is sensitive to both input features and outputs. Based on the derived features prediction accuracy can be greatly improved. We validate our approach in two applications. The first is to model users' preferences on a set of paintings. The second application is concerned with image categorization where each image may belong to multiple categories. The proposed algorithm produces very encouraging results in both settings.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-output regularized projection


    Contributors:
    Yu, K. (author) / Yu, S. (author) / Tresp, V. (author)


    Publication date :

    2005-01-01


    Size :

    175579 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Regularized least square discriminant projection and feature selection

    Shi, J. / Jiang, Z. / Zhao, D. et al. | British Library Online Contents | 2014



    Regularized Softmax Deep Multi−Agent Q−Learning

    Pan, L / Rashid, T / Peng, B et al. | BASE | 2021

    Free access

    PROJECTION SYSTEM FOR SMART RING VISUAL OUTPUT

    SANCHEZ KENNETH JASON | European Patent Office | 2023

    Free access

    Projection system for smart ring visual output

    SANCHEZ KENNETH JASON | European Patent Office | 2023

    Free access