An adaptive method for N-dimensional tensor factorization on sparse data processing

2017 ◽  
Author(s):  
Mu Li ◽  
Hao Yin ◽  
Yan Wang ◽  
Fengyue Gao ◽  
Chunxiao Cai
Keyword(s):  
Data Processing ◽  
Adaptive Method ◽  
Sparse Data ◽  
Tensor Factorization

Industrialization Research and Realization of Preprocessing in Rough Set Based on Similar Prediction

2012 ◽  
Vol 251 ◽  
pp. 185-190
Author(s):  
Dun Hong Yao ◽  
Xiao Ning Peng ◽  
Jia He
Keyword(s):  
Data Processing ◽  
Collaborative Filtering ◽  
Rough Set ◽  
Data Preprocessing ◽  
Sparse Data ◽  
Data Evaluation ◽  
Prediction Algorithm ◽  
Rating Prediction ◽  
Essential Problem ◽  
Data Processing Methods

In every field which needs data processing, the sparseness of data is an essential problem that should be resolved, especially in movies, shopping sites. The users with the same commodity preferences makes the data evaluation valuable. Otherwise, without any evaluation of information, it will result in sparse distribution of the entire data obtained. This article introduces a collaborative filtering technology used in sparse data processing methods - project-based rating prediction algorithm, and extends it to the areas of rough set, the sparse information table processing, rough set data preprocessing sparse issues.

scholarly journals A Track-Before-Detect Strategy Based on Sparse Data Processing for Air Surveillance Radar Applications

2021 ◽  
Vol 13 (4) ◽  
pp. 662
Author(s):  
Nicomino Fiscante ◽  
Pia Addabbo ◽  
Carmine Clemente ◽  
Filippo Biondi ◽  
Gaetano Giunta ◽  
...  
Keyword(s):  
Data Processing ◽  
Ad Hoc ◽  
Tracking System ◽  
Sparse Data ◽  
Sparse Learning ◽  
Multiple Target ◽  
Novel Approach ◽  
Air Surveillance ◽  
Reference Track ◽  
Track Before Detect

In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.

The Karolinska Hospital Information System

1974 ◽  
Vol 13 (03) ◽  
pp. 125-140 ◽  
Author(s):  
Ch. Mellner ◽  
H. Selajstder ◽  
J. Wolodakski
Keyword(s):  
Information System ◽  
Data Processing ◽  
Data Base ◽  
Hospital Information System ◽  
Care Delivery ◽  
Traditional Approach ◽  
Processing System ◽  
Systems Design ◽  
Data Processing System ◽  
Karolinska Hospital

The paper gives a report on the Karolinska Hospital Information System in three parts.In part I, the information problems in health care delivery are discussed and the approach to systems design at the Karolinska Hospital is reported, contrasted, with the traditional approach.In part II, the data base and the data processing system, named T1—J 5, are described.In part III, the applications of the data base and the data processing system are illustrated by a broad description of the contents and rise of the patient data base at the Karolinska Hospital.

Data Screening Methods – Application to Differential Diagnosis in Pancreatic Pathology from Radiological Signs

1978 ◽  
Vol 17 (01) ◽  
pp. 36-40 ◽  
Author(s):  
J.-P. Durbec ◽  
Jaqueline Cornée ◽  
P. Berthezene
Keyword(s):  
Differential Diagnosis ◽  
Mutual Information ◽  
Data Processing ◽  
Screening Methods ◽  
Automatic Data ◽  
Chronic Calcifying Pancreatitis ◽  
Number Of Patients ◽  
Calcifying Pancreatitis ◽  
Pancreatic Pathology ◽  
Data Screening

The practice of systematic examinations in hospitals and the increasing development of automatic data processing permits the storing of a great deal of information about a large number of patients belonging to different diagnosis groups.To predict or to characterize these diagnosis groups some descriptors are particularly useful, others carry no information. Data screening based on the properties of mutual information and on the log cross products ratios in contingency tables is developed. The most useful descriptors are selected. For each one the characterized groups are specified.This approach has been performed on a set of binary (presence—absence) radiological variables. Four diagnoses groups are concerned: cancer of pancreas, chronic calcifying pancreatitis, non-calcifying pancreatitis and probable pancreatitis. Only twenty of the three hundred and forty initial radiological variables are selected. The presence of each corresponding sign is associated with one or more diagnosis groups.

Sign in / Sign up

Export Citation Format

Share Document