Combining ELM with Random Projections for Low and High Dimensional Data Classification and Clustering

被引：4

作者：

Alshamiri, Abobakr Khalil ^{[1
]}

Singh, Alok ^{[1
]}

Surampudi, Bapi Raju ^{[1
,2
]}

机构：

[1] Univ Hyderabad, Sch Comp & Informat Sci, Hyderabad 500046, Andhra Pradesh, India

[2] Int Inst Informat Technol, Cognit Sci Lab, Hyderabad 500032, Andhra Pradesh, India

来源：

PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON FUZZY AND NEURO COMPUTING (FANCCO - 2015) | 2015年 / 415卷

关键词：

Extreme learning machine; Random projection; Classification; Clustering; EXTREME LEARNING-MACHINE;

D O I：

10.1007/978-3-319-27212-2_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Extreme learning machine (ELM), as a new learning method for training feedforward neural networks, has shown its good generalization performance in regression and classification applications. Random projection (RP), as a simple and powerful technique for dimensionality reduction, is used for projecting high-dimensional data into low-dimensional subspaces while ensuring that the distances between data points are approximately preserved. This paper presents a systematic study on the application of RP in conjunction with ELM for both low-and high-dimensional data classification and clustering.

引用

页码：89 / 107

页数：19

共 50 条

[21] Biomedical Data Ensemble Classification using Random Projections
Tasoulis, Sotiris K.
Vrahatis, Aristidis G.
Georgakopoulos, Spiros V.
Plagianakos, Vassilis P.
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 166 - 172
[22] A directional clustering technique for random data classification
Reyes, C
Adjouadi, M
CYTOMETRY, 1997, 27 (02): : 126 - 135
[23] Clustering Lines in High-Dimensional Space: Classification of Incomplete Data
Gao, Jie
Langberg, Michael
Schulman, Leonard J.
ACM TRANSACTIONS ON ALGORITHMS, 2010, 7 (01)
[24] ON THE CONDITIONAL DISTRIBUTIONS OF LOW-DIMENSIONAL PROJECTIONS FROM HIGH-DIMENSIONAL DATA
Leeb, Hannes
ANNALS OF STATISTICS, 2013, 41 (02): : 464 - 483
[25] ON ALMOST LINEARITY OF LOW-DIMENSIONAL PROJECTIONS FROM HIGH-DIMENSIONAL DATA
HALL, P
LI, KC
ANNALS OF STATISTICS, 1993, 21 (02): : 867 - 889
[26] Optimal projections of high dimensional data
Corchado, E
Fyfe, C
2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2002, : 589 - 596
[27] An efficient approach for feature construction of high-dimensional microarray data by random projections
Tariq, Hassan
Eldridge, Elf
Welch, Ian
PLOS ONE, 2018, 13 (04):
[28] GA-optimized random forest classification for high dimensional data
Pan, Jingchang
Wei, Peng
Guo, Qiang
Zhang, Caiming
Luo, Ali
ICIC Express Letters, 2011, 5 (05): : 1529 - 1534
[29] Classification by ensembles from random partitions of high-dimensional data
Ahn, Hongshik
Moon, Hojin
Fazzari, Melissa J.
Lim, Noha
Chen, James J.
Kodell, Ralph L.
COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2007, 51 (12) : 6166 - 6179
[30] Clustering high dimensional data
Assent, Ira
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, 2012, 2 (04) : 340 - 350

← 1 2 3 4 5 →