Results 271 to 280 of about 1,131,696 (312)

Development of a flavor fingerprint by HS-GC-IMS with PCA for volatile compounds of Tricholoma matsutake Singer.

Food Chemistry, 2019
The flavor fingerprint of Tricholoma matsutake Singer was developed and volatile compounds were investigated by HS-GC-IMS fingerprinting combining with PCA. A total of 25 typical target compounds were identified.
Mengqi Li, Ruiwen Yang, Hao Zhang, Silu Wang, Dong Chen, Songyi Lin   +5 more
semanticscholar   +1 more source

Dimension reduction of image deep feature using PCA

Journal of Visual Communication and Image Representation, 2019
Convolution neural networks based methods can derive deep features from training images. However, one challenge is that the dimension of the extracted image features increases dramatically with more network layers.
Ji Ma, Yuyu Yuan
semanticscholar   +1 more source

Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index

Tourism Management, 2018
The precise forecasting of tourist volume is a very challenging task. This paper aims to propose an effective model named PCA-ADE-BPNN for forecasting tourist volume based on Baidu index.
Shaowen Li, Tao Chen, Lin Wang, Chenghan Ming   +3 more
semanticscholar   +1 more source

Parallel PCA–KPCA for nonlinear process monitoring

Control Engineering Practice, 2018
Both linear and nonlinear relationships may exist among process variables, and monitoring a process with such complex relationships among variables is imperative. However, individual principal component analysis (PCA) or kernel PCA (KPCA) may not be able
Qingchao Jiang, Xue-feng Yan
semanticscholar   +1 more source

Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network

Journal of Process Control, 2018
A prediction model based on the principal component analysis (PCA) and back propagation (BP) neural network is proposed for BOF end-point phosphorus content, based on the characters of BOF metallurgical process and production data.
F. He, Ling-ying Zhang
semanticscholar   +1 more source

PCA and kernel PCA

2014
Introduction Two primary techniques for dimension-reducing feature extraction are subspace projection and feature selection . This chapter will explore the key subspace projection approaches, i.e. PCA and KPCA. (i) Section 3.2 provides motivations for dimension reduction by pointing out (1) the potential adverse effect of large feature ...
openaire   +1 more source

Local PCA algorithms

IEEE Transactions on Neural Networks, 2000
Within the last years various principal component analysis (PCA) algorithms have been proposed. In this paper we use a general framework to describe those PCA algorithms which are based on Hebbian learning. For an important subset of these algorithms, the local algorithms, we fully describe their equilibria, where all lateral connections are set to ...
A, Weingessel, K, Hornik
openaire   +2 more sources

Robust PCAs and PCA Using Generalized Mean

2017
In this chapter, a robust principal component analysis (PCA) is described, which can overcome the problem that PCA is prone to outliers included in training set. Different from the other alternatives which commonly replace \(L_{2}\)-norm by other distance measures, our method alleviates the negative effect of outliers using the characteristic of the ...
Jiyong Oh, Nojun Kwak
openaire   +1 more source

Subcutaneous-PCA

The Clinical Journal of Pain, 1990
Patients (n = 120) undergoing major orthopedic (e.g., total hip replacement), urologic (e.g., radical prostatectomy), or gynecologic (e.g., total abdominal hysterectomy) procedures were randomly assigned to receive either morphine or oxymorphone postoperatively using a patient-controlled analgesic (PCA) delivery system.
openaire   +2 more sources