Improved algorithms for high-dimensional robust PCA

Lin, Xiaoyong; Zhang, Zeqiu; Wang, Jue; Zhang, Zhaoyang; Qiu, Tingting; Mi, Zhengkun

doi:10.1109/icuwb.2016.7790402

Cited by 2 publications

(2 citation statements)

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“…Whether the data consists of images for hyper spectral imaging, or industrial data for industrial purposes, dimensionality reduction of such data is crucial if not compulsory because their size/dimensionality can range from thousands to hundreds of thousands [3]. Traditional PCA has been globally recognized as an easy and accurate dimensionality reduction technique [4]. It develops the best approximation in the subspace, in terms of observations, in a way that is least-square.…”

Section: Introductionmentioning

confidence: 99%

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Improved Dimensionality Reduction of Various Datasets Using Novel Multiplicative Factoring Principal Component Analysis (MPCA)

Ogbuanya¹

2021

IJCCE

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Principal Component Analysis (PCA) is known to be the most widely applied dimensionality reduction approach. A lot of improvements have been done on the traditional PCA, in order to obtain optimal results in the dimensionality reduction of various datasets. In this paper, we present an improvement to the traditional PCA approach called Multiplicative factoring Principal Component Analysis (MPCA). The advantage of MPCA over the traditional PCA is that a penalty is imposed on the occurrence space through a multiplier to make negligible the effect of outliers in seeking out projections. Here we apply two multiplier approaches, total distance and cosine similarity metrics. These two approaches can learn the relationship that exists between each of the data points and the principal projections in the feature space. As a result of this, improved low-rank projections are gotten through multiplying the data iteratively to make negligible the effect of corrupt data in the training set. Experiments were carried out on YaleB, MNIST, AR, and Isolet datasets and the results were compared to results gotten from some popular dimensionality reduction methods such as traditional PCA, RPCA-OM, and also some recently published methods such as IFPCA-1 and IFPCA-2.

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Section: Introductionmentioning

confidence: 99%

“…It develops the best approximation in the subspace, in terms of observations, in a way that is least-square. It achieves this by calculating singular value decomposition of the first dataset [4]. As a result of its quadratic error criterion, PCA is sensitive to outliers, in the face of datasets.…”

Section: Introductionmentioning

confidence: 99%