자료유형  

 학위논문

Control Number  

0015490755

International Standard Book Number  

9781085567510

Dewey Decimal Classification Number  

001

Main Entry-Personal Name  

Aggour, Kareem Sherif.

Publication, Distribution, etc. (Imprint  

[Sl] : Rensselaer Polytechnic Institute, 2019

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2019

Physical Description  

168 p

General Note  

Source: Dissertations Abstracts International, Volume: 81-02, Section: B.

General Note  

Advisor: Yener, Bulent

Dissertation Note  

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2019.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Over the past two decades, there has been a dramatic increase in the volume and variety of data generated in almost every scientific discipline. To enable the efficient storage and processing of these massive datasets, a variety of fault tolerant, scalable distributed storage and processing platforms have been popularized---most famously, Hadoop MapReduce and Spark. Novel distributed algorithms are being developed to take full advantage of these platforms, including scalable variants of algorithms such as Canonical Polyadic Decomposition (CPD), an unsupervised learning technique frequently used in data mining and machine learning applications to discover latent factors in a class of multimodal datasets referred to as tensors. Current research in scalable CPD algorithms have focused almost exclusively on the analysis of large sparse tensors, however.This research addresses the complementary need for efficient, scalable algorithms to decompose large dense tensors that arise in many signal processing and anomaly detection applications. To that end, we developed a progression of algorithms designed for MapReduce settings that incorporate combinations of regularization and sketching to efficiently operate on dense, skewed tensors. The first MapReduce CPD algorithm utilizes an Alternating Least Squares (ALS) strategy that is mathematically equivalent to the classical sequential CPD-ALS algorithm. A second algorithm was then developed that features regularization and sketching working in tandem to accelerate and stabilize tensor decompositions. Prior research had demonstrated the benefits of applying either regularization or sketching to CPD-ALS, but to our knowledge this work is the first to demonstrate the utility of using both together, outperforming the use of either technique alone. However, this algorithm requires the manual selection of the sketching and regularization hyperparameter values.We next developed two novel algorithms that employ online learning-based approaches to dynamically select the sketching rate and regularization parameters at runtime, further optimizing CP decompositions while simultaneously eliminating the burden of manual hyperparameter selection. This work is the first to intelligently choose the sketching rate and regularization parameters at each iteration of a CPD algorithm to balance the trade-off between minimizing the runtime and maximizing the decomposition accuracy. On both synthetic and real data, it was observed that for noisy tensors, our intelligent CPD algorithm produces decompositions of accuracy comparable to the exact distributed CPD-ALS algorithm in less time, often half the time. For ill-conditioned tensors, given the same time budget, the intelligent CPD algorithm produces decompositions with significantly lower relative error, often yielding an order of magnitude improvement.

Subject Added Entry-Topical Term  

Computer science

Subject Added Entry-Topical Term  

Artificial intelligence

Added Entry-Corporate Name  

Rensselaer Polytechnic Institute Computer Science

Host Item Entry  

Dissertations Abstracts International. 81-02B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:566958