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<i>Sanicula fengjiensis</i> (Apiaceae), a New Species From China Revealed by Integrative Morphological and Molecular Evidence. [PDF]

Ecol Evol
Li HM, Liao JM, Ma XD, Song CF.
europepmc   +1 more source

A New Framework to Empower Ecosystem Assessment Through the Integration of eDNA Inventories, Graph Theory and Niche Modelling. [PDF]

Ecol Lett
Alric B, Domaizon I, Laplace-Treyture C, Viollaz L, Rimet F.   +4 more
europepmc   +1 more source

Sparse ICA via Cluster-Wise PCA [PDF]

Neurocomputing, 2006
In this paper, it is shown that independent component analysis (ICA) of sparse signals (sparse ICA) can be seen as a cluster-wise principal component analysis (PCA). Consequently, Sparse ICA may be done by a combination of a clustering algorithm and PCA. For the clustering part, we use, in this paper, an algorithm inspired from K-means.
Massoud Babaie-Zadeh, Christian Jutten, Ali Mansour   +2 more
exaly   +5 more sources

Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incremental PCA Algorithms for Heart Failure Prediction

, 2020
Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction.
Liaqat Ali, Shafqat Ullah Khan, Muhammad Umair Khan   +2 more
exaly   +2 more sources

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Two-Dimensional Quaternion PCA and Sparse PCA

IEEE Transactions on Neural Networks and Learning Systems, 2019
Benefited from quaternion representation that is able to encode the cross-channel correlation of color images, quaternion principle component analysis (QPCA) was proposed to extract features from color images while reducing the feature dimension. A quaternion covariance matrix (QCM) of input samples was constructed, and its eigenvectors were derived to
Xiaolin Xiao, Yicong Zhou
openaire   +2 more sources

PCA in Sparse Data-Dependent Noise

2018 IEEE International Symposium on Information Theory (ISIT), 2018
In recent work, we obtained finite sample guarantees for the problem of Principal Component Analysis (PCA) in nonisotropic and data-dependent noise. In this work, we study an important special case of this: the problem of PCA in sparse data-dependent noise with the noise depending linearly on the signal (true data) at each time.
Praneeth Narayanamurthy, Namrata Vaswani
exaly   +2 more sources

Sparse PCA by iterative elimination algorithm

Advances in Computational Mathematics, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang Wang 0020, Qiang Wu 0003
openaire   +3 more sources

Sparse Bayesian Learning for Robust PCA

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
In this paper, we propose a new Bayesian model to solve the Robust PCA problem - recovering the underlying low-rank matrix and sparse matrix from their noisy compositions. We first derive and analyze a new objective function, which is proven to be equivalent to the fundamental minimizing "rank+sparsity" objective.
Jing Liu 0009, Yacong Ding, Bhaskar Rao 0001   +2 more
openaire   +2 more sources

Vector $l_0$ Sparse Variable PCA

IEEE Transactions on Signal Processing, 2011
Principal component analysis (PCA) achieves dimension reduction by replacing the original measured variables with a smaller set of derived variables called the principal components. Sparse PCA improves this with sparsity. There are two kinds of sparse PCA; sparse loading PCA (slPCA) which keeps all the measured variables but zeroes out some of their ...
Magnus O. Ulfarsson, Victor Solo
openaire   +1 more source