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sPCA: Scalable Principal Component Analysis for Big Data on Distributed Platforms

Summary: Introduces sPCA, a scalable PCA optimized for distributed big-data platforms. Leverages sparse matrix ops, minimizes intermediates, and is implemented on MapReduce and Spark; outperforms Mahout-PCA and MLlib-PCA in accuracy, speed, and data-shuffle. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5085
Venue
SIGMOD
Year
2015
Pagerank
4.5392079e-05
Overall Rank
8,294 | 42.36%
DOI
10.1145/2723372.2751520

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,044 QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models 2024 VLDB 4.1905499e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
408 HaLoop: Efficient Iterative Data Processing on Large Clusters 2010 VLDB 0.00023939456
543 MLbase: A Distributed Machine-learning System 2013 CIDR 0.0002050918
1,878 ArrayStore: A Storage Manager for Complex Parallel Array Processing 2011 SIGMOD 0.00010229691
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