Real-Time Principal Component Analysis

被引：1

作者：

Chowdhury, Ranak Roy ^{[1
]}

Adnan, Muhammad Abdullah ^{[2
]}

Gupta, Rajesh K. ^{[1
]}

机构：

[1] University of California San Diego, Halıcıoglu Data Science Institute, United States

[2] Bangladesh University of Engineering and Technology (BUET), Bangladesh

来源：

ACM/IMS Transactions on Data Science | 2020年 / 1卷 / 02期

关键词：

We propose a variant of Principal Component Analysis (PCA) that is suited for real-time applications. In the real-time version of the PCA problem; we maintain a window over the most recent data and project every incoming row of data into a lower-dimensional subspace; which we generate as the output of the model. The goal is to reduce the reconstruction error of the output from the input and to retain major components pertaining to previous distributions of the data. We use the reconstruction error as the termination criteria to update the eigenspace as new data arrives. We then propose two variants of this algorithm that are progressively more time efficient. To verify whether our proposed model can capture the essence of the changing distribution of large datasets in real time; we have implemented the algorithms and compared performance against carefully designed simulations that change distributions of data sources over time in a controllable manner. Furthermore; we have demonstrated that proposed algorithms can capture the changing distributions of real-life datasets by running simulations on datasets from a variety of real-time applications; e.g; localization; activity recognition; customer expenditure; and so forth. Results show that straightforward modifications to convert PCA to use a sliding window of datasets do not work because of the difficulties associated with determination of optimal window size. Instead; we propose algorithmic enhancements that rely on spectral analysis to improve dimensionality reduction. Results show that our methods can successfully capture the changing distribution of data in a real-time scenario; thus enabling real-time PCA. © 2020 ACM;

D O I：

10.1145/3374750

中图分类号：

学科分类号：

摘要：

引用

共 50 条

[1] Real-Time Principal Component Pursuit
Pope, Graeme
Baumann, Manuel
Studer, Christoph
Durisi, Giuseppe
2011 CONFERENCE RECORD OF THE FORTY-FIFTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS (ASILOMAR), 2011, : 1433 - 1437
[2] Real-time PCA(Principal component analysis) implementation on DSP
Han, DH
Rao, YN
Principe, JC
Gugel, K
2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2004, : 2159 - 2162
[3] Real Time Principal Component Analysis
Chowdhury, Ranak Roy
Adnan, Muhammad Abdullah
Gupta, Rajesh K.
2019 IEEE 35TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2019), 2019, : 1678 - 1681
[4] Real-time monitoring and characterization of flames by principal-component analysis
Sbarbaro, D
Farias, O
Zawadsky, A
COMBUSTION AND FLAME, 2003, 132 (03) : 591 - 595
[5] New fast principal component analysis for real-time face detection
El-Bakry, Hazem M.
Machine Graphics and Vision, 2009, 18 (04): : 405 - 425
[6] Real-time fault detection and diagnosis using sparse principal component analysis
Gajjar, Shriram
Kulahci, Murat
Palazoglu, Ahmet
JOURNAL OF PROCESS CONTROL, 2018, 67 : 112 - 128
[7] Real-time face recognition by the PCA (principal component analysis) with color images
Kim, JO
Seo, SJ
Chung, CH
COMPUTATIONAL SCIENCE - ICCS 2004, PT 1, PROCEEDINGS, 2004, 3036 : 687 - 690
[8] Real-time recognition of hand alphabet gestures using principal component analysis
Birk, H
Moeslund, TB
Madsen, CB
SCIA '97 - PROCEEDINGS OF THE 10TH SCANDINAVIAN CONFERENCE ON IMAGE ANALYSIS, VOLS 1 AND 2, 1997, : 261 - 268
[9] Weighted Principal Component Analysis for Real-Time Background Removal in GPR Data
Shkolnikov, Yakov P.
DETECTION AND SENSING OF MINES, EXPLOSIVE OBJECTS, AND OBSCURED TARGETS XVII, 2012, 8357
[10] Real-Time Digital Implementation of a Principal Component Analysis Algorithm for Neurons Spike Detection
Vallicelli, E. A.
Fary, F.
Baschirotto, A.
de Matteis, M.
Reato, M.
Maschietto, M.
Rocchi, F.
Vassanelli, S.
Guarrera, D.
Collazuol, G.
Zeitler, R.
2018 INTERNATIONAL CONFERENCE ON IC DESIGN AND TECHNOLOGY (ICICDT 2018), 2018, : 33 - 36

← 1 2 3 4 5 →