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Title: Diverse Power Iteration Embeddings and Its Applications

Abstract—Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings (DPIE), which not only retains the similar efficiency of power iteration methods but also produces a series of diverse and more effective embedding vectors. We test this novel method by applying it to various data mining applications (e.g. clustering, anomaly detection and feature selection) and evaluating their performance improvements. The experimental results show our proposed DPIE is more effective than popular spectral approximation methods, and obtains the similar quality of classic spectral embedding derived from eigen-decompositions. Moreover it is extremely fast on big data applications. For example in terms of clustering result, DPIE achieves as good as 95% of classic spectral clustering on the complex datasets but 4000+ times faster in limited memory environment.
Authors:
; ; ;
Publication Date:
OSTI Identifier:
1164792
Report Number(s):
BNL--107039-2014-CP
DOE Contract Number:
DE-AC02-98CH10886
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Data Mining (ICDM) 2014; Shenzhen, China; 20141214 through 20141217
Research Org:
Brookhaven National Laboratory (BNL)
Sponsoring Org:
USDOE SC OFFICE OF ADVANCED SCIENTIFIC COMPUTING RESEARCH
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING