Evidence map›Paper›PMID 26161960›Full record

ArticlePloS one2015

Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks.

Shengqiao Ni, Jiancheng Lv, Zhehao Cheng, Mao Li

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shengqiao NiMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
Jiancheng LvMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
Zhehao ChengMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
Mao LiMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.

Indexed as

AlgorithmsMultifactor Dimensionality ReductionNeural Networks, ComputerComputational BiologyComputer SimulationNonlinear DynamicsReproducibility of Results

Identifiers

PMID26161960
PMCPMC4498733

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.