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of 21
pro vyhledávání: '"Huang, Su"'
Over the years, numerous rank estimators for factor models have been proposed in the literature. This article focuses on information criterion-based rank estimators and investigates their consistency in rank selection. The gap conditions serve as nec
Externí odkaz:
http://arxiv.org/abs/2407.19959
Principal component analysis (PCA) is a widely used dimension reduction method, but its performance is known to be non-robust to outliers. Recently, product-PCA (PPCA) has been shown to possess the efficiency-loss free ordering-robustness property: (
Externí odkaz:
http://arxiv.org/abs/2407.19725
Autor:
Hung, Hung, Huang, Su-Yun
This article studies the robustness of the eigenvalue ordering, an important issue when estimating the leading eigen-subspace by principal component analysis (PCA). In Yata and Aoshima (2010), cross-data-matrix PCA (CDM-PCA) was proposed and shown to
Externí odkaz:
http://arxiv.org/abs/2302.11124
Principal component analysis (PCA) is one of the most popular dimension reduction methods. The usual PCA is known to be sensitive to the presence of outliers, and thus many robust PCA methods have been developed. Among them, the Tyler's M-estimator i
Externí odkaz:
http://arxiv.org/abs/2206.03662
Principal component analysis (PCA) is the most commonly used statistical procedure for dimension reduction. An important issue for applying PCA is to determine the rank, which is the number of dominant eigenvalues of the covariance matrix. The Akaike
Externí odkaz:
http://arxiv.org/abs/2004.13914
Autor:
Morimoto, Toshinari, Huang, Su-Yun
In this paper, we propose a dimensionality reduction method applied to tensor-structured data as a hidden layer (we call it TensorProjection Layer) in a convolutional neural network. Our proposed method transforms input tensors into ones with a small
Externí odkaz:
http://arxiv.org/abs/2004.04454
Publikováno v:
Physical Review Applied, 13, 014052 (2020)
Organic-inorganic hybrid halide perovskites have ignited tremendous interests for photovoltaic applications. However, their nonlinear optical response has not been studied although many of these structures lack the centrosymmetry and exhibit ferroele
Externí odkaz:
http://arxiv.org/abs/2002.05216
The singular value decomposition (SVD) and the principal component analysis are fundamental tools and probably the most popular methods for data dimension reduction. The rapid growth in the size of data matrices has lead to a need for developing effi
Externí odkaz:
http://arxiv.org/abs/2001.11874
Publikováno v:
Annals of Mathematical Sciences and Applications. Volume 5, Number 2, 283-316, 2020
Principal component analysis (PCA) is arguably the most widely used dimension-reduction method for vector-type data. When applied to a sample of images, PCA requires vectorization of the image data, which in turn entails solving an eigenvalue problem
Externí odkaz:
http://arxiv.org/abs/1911.09816
In big data analysis, a simple task such as linear regression can become very challenging as the variable dimension $p$ grows. As a result, variable screening is inevitable in many scientific studies. In recent years, randomized algorithms have becom
Externí odkaz:
http://arxiv.org/abs/1801.00105