Incomplete-Data Oriented Dimension Reduction via Instance Factoring PCA Framework

  • Ernest Domanaanmwi Ganaa
  • Timothy Apasiba Abeo
  • Sumet Mehta
  • Heping Song
  • Xiang-Jun ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11903)


In this paper, we propose an instance factoring PCA (IFPCA) framework for dimension reduction in incomplete datasets. The advantage of IFPCA over the traditional PCA is that, a penalty is imposed on the instance space via a scaling-factor to suppress the effect of outliers in pursuing projections. We geometrically use two scaling-factor strategies, total distance and cosine similarity metrics. Both strategies can learn the relationship between each data point and the principal projection in the feature space. In this way, better low-rank projections are obtained through scaling the data iteratively to suppress the impact of noise in the training set. Extensive experiments on COIL-20, ORL and USPS datasets prove the superiority of the proposed framework over state-of-the-art dimensionality reduction methods such as LSDA, gLPCA, RPCA-OM, PCA, LPP and RCDA.


Principal component analysis Instance factoring Incomplete-data Dimensionality reduction Manifold learning 



This work was funded in part by the National Natural Science Foundation of China(No. 61572240) and Natural Science Foundation of Jiangsu Province (No. BK20170558).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ernest Domanaanmwi Ganaa
    • 1
    • 3
  • Timothy Apasiba Abeo
    • 1
    • 4
  • Sumet Mehta
    • 1
  • Heping Song
    • 1
  • Xiang-Jun Shen
    • 1
    • 2
    Email author
  1. 1.School of Computer Science and Communication EngineeringJiangSu UniversityZhenjiangChina
  2. 2.Jingkou New-Generation Information Technology Industry InstituteJiangSu UniversityZhenjiangChina
  3. 3.School of Applied Science and TechnologyWa PolytechnicWaGhana
  4. 4.School of Applied ScienceTamale Technical UniversityTamaleGhana

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