TY - GEN

T1 - Scalable partial least squares regression on grammar-compressed data matrices

AU - Tabei, Yasuo

AU - Saigo, Hiroto

AU - Yamanishi, Yoshihiro

AU - Puglisi, Simon J.

N1 - Funding Information:
This work was supported byMEXT/JSPS Kakenhi (24700140, 25700004 and 25700029), the JST PRESTO program, the Program to Disseminate Tenure Tracking System, MEXT and Kyushu University Interdisciplinary Programs in Education and Projects in Research Development, and the Academy of Finland via grant 294143
Publisher Copyright:
© 2016 ACM.

PY - 2016/8/13

Y1 - 2016/8/13

N2 - With massive high-dimensional data now commonplace in research and industry, there is a strong and growing demand for more scalable computational techniques for data analysis and knowledge discovery. Key to turning these data into knowledge is the ability to learn statistical models with high interpretability. Current methods for learning statistical models either produce models that are not interpretable or have prohibitive computational costs when applied to massive data. In this paper we address this need by presenting a scalable algorithm for partial least squares regression (PLS), which we call compression-based PLS (cPLS), to learn predictive linear models with a high interpretability from massive high-dimensional data. We propose a novel grammar-compressed representation of data matrices that supports fast row and column access while the data matrix is in a compressed form. The original data matrix is grammarcompressed and then the linear model in PLS is learned on the compressed data matrix, which results in a significant reduction in working space, greatly improving scalability. We experimentally test cPLS on its ability to learn linear models for classification, regression and feature extraction with various massive high-dimensional data, and show that cPLS performs superiorly in terms of prediction accuracy, computational effciency, and interpretability.

AB - With massive high-dimensional data now commonplace in research and industry, there is a strong and growing demand for more scalable computational techniques for data analysis and knowledge discovery. Key to turning these data into knowledge is the ability to learn statistical models with high interpretability. Current methods for learning statistical models either produce models that are not interpretable or have prohibitive computational costs when applied to massive data. In this paper we address this need by presenting a scalable algorithm for partial least squares regression (PLS), which we call compression-based PLS (cPLS), to learn predictive linear models with a high interpretability from massive high-dimensional data. We propose a novel grammar-compressed representation of data matrices that supports fast row and column access while the data matrix is in a compressed form. The original data matrix is grammarcompressed and then the linear model in PLS is learned on the compressed data matrix, which results in a significant reduction in working space, greatly improving scalability. We experimentally test cPLS on its ability to learn linear models for classification, regression and feature extraction with various massive high-dimensional data, and show that cPLS performs superiorly in terms of prediction accuracy, computational effciency, and interpretability.

UR - http://www.scopus.com/inward/record.url?scp=84984985641&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84984985641&partnerID=8YFLogxK

U2 - 10.1145/2939672.2939864

DO - 10.1145/2939672.2939864

M3 - Conference contribution

AN - SCOPUS:84984985641

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 1875

EP - 1884

BT - KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

T2 - 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016

Y2 - 13 August 2016 through 17 August 2016

ER -