Conference proceeding
Big Data Analytics: Optimization and Randomization
KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, pp.2327-2327
01/01/2015
DOI: 10.1145/2783258.2789989
Abstract
As the scale and dimensionality of data continue to grow in many applications of data analytics (e.g., bioinformatics, finance, computer vision, medical informatics), it becomes critical to develop efficient and effective algorithms to solve numerous machine learning and data mining problems. This tutorial will focus on simple yet practically effective techniques and algorithms for big data analytics. In the first part, we plan to present the state-of-the-art large-scale optimization algorithms, including various stochastic gradient descent methods, stochastic coordinate descent methods and distributed optimization algorithms, for solving various machine learning problems. In the second part, we will focus on randomized approximation algorithms for learning from large-scale data. We will discuss i) randomized algorithms for low-rank matrix approximation; ii) approximation techniques for solving kernel learning problems; iii) randomized reduction methods for addressing the high-dimensional challenge. Along with the description of algorithms, we will also present some empirical results to facilitate understanding of different algorithms and comparison between them.
Details
- Title: Subtitle
- Big Data Analytics: Optimization and Randomization
- Creators
- Tianbao Yang - University of IowaQihang Lin - University of IowaRong Jin - Michigan State University
- Resource Type
- Conference proceeding
- Publication Details
- KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, pp.2327-2327
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/2783258.2789989
- Number of pages
- 1
- Language
- English
- Date published
- 01/01/2015
- Academic Unit
- Business Analytics; Computer Science
- Record Identifier
- 9984380608102771
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