Tensor Factorization and Clustering for the Feature Extraction Based on Tucker3 with Updating Core

2011 ◽  
Vol 308-310 ◽  
pp. 2517-2522 ◽  
Author(s):  
Hai Jun Wang ◽  
Fei Yun Xu ◽  
Fei Wang
Keyword(s):  
Feature Extraction ◽  
Parallel Computing ◽  
Large Scale ◽  
Tensor Factorization ◽  
Parallel Mode ◽  
Large Scale Tensor

Aiming at the problems of Tucker3 to large-scale tensor when applied to feature extraction, a new factorization based on Tucker3 is proposed to extract feature from the tensors. First, the large-scale tensor is divided into multiple sub-tensors so as to conveniently compute cores of sub-tensors in parallel mode with Matlab Parallel Computing Toolbox; Then, the cores of each sub-tensor are updated for reducing deviation in calculating and the similar characteristics of sub-tensors are clustered to obtain the features. Experiment results show that this methods is able to extract features rapidly and efficiently.

scholarly journals Large scale tensor regression using kernels and variational inference

2021 ◽  
Author(s):  
Robert Hu ◽  
Geoff K. Nicholls ◽  
Dino Sejdinovic
Keyword(s):  
Large Scale ◽  
Side Information ◽  
Variational Inference ◽  
Superior Performance ◽  
Tensor Factorization ◽  
Latent Factors ◽  
Uncertainty Estimates ◽  
Novel Method ◽  
Prediction And Forecasting ◽  
Large Scale Tensor

AbstractWe outline an inherent flaw of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this. We coin our methodology kernel fried tensor (KFT) and present it as a large-scale prediction and forecasting tool for high dimensional data. Our results show superior performance against LightGBM and Field aware factorization machines (FFM), two algorithms with proven track records, widely used in large-scale prediction. We also develop a variational inference framework for KFT which enables associating the predictions and forecasts with calibrated uncertainty estimates on several datasets.

Thermal-Scalable 3D Parallel-Heat-Sinking Integration Methodology: Key SoC Technology for Large-Scale Parallel Computing

2011 ◽  
Vol 34 (4) ◽  
pp. 717-728
Author(s):  
Zu-Ying LUO ◽  
Yin-He HAN ◽  
Guo-Xing ZHAO ◽  
Xian-Chuan YU ◽  
Ming-Quan ZHOU
Keyword(s):  
Parallel Computing ◽  
Large Scale ◽  
Heat Sinking

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

Feature Extraction for Large-Scale Text Collections

2020 ◽  
Author(s):  
Luke Gallagher ◽  
Antonio Mallia ◽  
J. Shane Culpepper ◽  
Torsten Suel ◽  
B. Barla Cambazoglu
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Text Collections

THE CONNECTION MACHINE IN A UNIVERSITY ENVIRONMENT: GESAMTHOCHSCHULE WUPPERTAL

1993 ◽  
Vol 04 (01) ◽  
pp. 137-141
Author(s):  
KLAUS SCHILLING
Keyword(s):  
Parallel Computing ◽  
Computer Science ◽  
Large Scale ◽  
Early History ◽  
Short Account ◽  
University Environment ◽  
Connection Machine ◽  
The University

A short account is presented on the early history, the intentions and the development of large scale parallel computing at the University of Wuppertal. It might serve as an illustration how common activities between computational and computer science can be stimulated, in the university environment.

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