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Adaptive low-nonnegative-rank approximation for state aggregation of markov chains
Yaqi Duan
,
Mengdi Wang
, Zaiwen Wen
, Yaxiang Yuan
Electrical and Computer Engineering
Center for Statistics & Machine Learning
Princeton Language and Intelligence (PLI)
Research output
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Contribution to journal
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Article
›
peer-review
5
Scopus citations
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Dive into the research topics of 'Adaptive low-nonnegative-rank approximation for state aggregation of markov chains'. Together they form a unique fingerprint.
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Mathematics
Markov Chain
100%
Transition Matrix
100%
Factorization
100%
State Markov Chain
50%
Matrix (Mathematics)
50%
Statistical Property
50%
Synthetic Data
50%
Global Solution
50%
Subgradient
50%
Linear Combination
50%
Local Minimum
50%
Approximation Method
50%
Minimization Problem
50%
Keyphrases
State Transition Matrix
50%
Low-rank Minimization
25%
Rank Minimization
25%
Aggregation Structure
25%
Additional Column
25%
Proximal Alternating Linearized Minimization
25%
Nuclear Norm Relaxation
25%
Computer Science
Norm Relaxation
50%
Data Transportation
50%