Robust Non-Negative Matrix Factorization under Separability Assumption

2016 ◽  
pp. 7-1-7-15
Keyword(s):  
Matrix Factorization ◽  
Separability Assumption ◽  
Non Negative Matrix Factorization

scholarly journals Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization

2018 ◽  
Author(s):  
Yuxuan Du ◽  
Tongliang Liu ◽  
Yinan Li ◽  
Runyao Duan ◽  
Dacheng Tao
Keyword(s):  
Matrix Factorization ◽  
Computational Cost ◽  
Selection Procedure ◽  
Quantum Algorithm ◽  
Divide And Conquer ◽  
Plausible Assumption ◽  
Separability Assumption ◽  
Data Points ◽  
Fixed Rank ◽  
Non Negative Matrix Factorization

It is NP-complete to find non-negative factors W and H with fixed rank r from a non-negative matrix X by minimizing ||X-WH^Τ ||^2. Although the separability assumption (all data points are in the conical hull of the extreme rows) enables polynomial-time algorithms, the computational cost is not affordable for big data. This paper investigates how the power of quantum computation can be capitalized to solve the non-negative matrix factorization with the separability assumption (SNMF) by devising a quantum algorithm based on the divide-and-conquer anchoring (DCA) scheme [Zhou et al., 2013]. The design of quantum DCA (QDCA) is challenging. In the divide step,  the random projections in  DCA is completed by a quantum algorithm for linear operations, which achieves the exponential speedup. We then  devise a heuristic post-selection procedure which extracts the information of anchors stored in the quantum states efficiently. Under a plausible assumption, QDCA performs efficiently, achieves the quantum speedup, and is beneficial for high dimensional problems.

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