Quantum Relief algorithm

@article{Liu2018QuantumRA,
  title={Quantum Relief algorithm},
  author={Wenjie Liu and Peipei Gao and Wenbin Yu and Zhiguo Qu and Ching-Nung Yang},
  journal={Quantum Information Processing},
  year={2018},
  volume={17},
  pages={1-15}
}
Relief algorithm is a feature selection algorithm used in binary classification proposed by Kira and Rendell, and its computational complexity remarkably increases with both the scale of samples and the number of features. In order to reduce the complexity, a quantum feature selection algorithm based on Relief algorithm, also called quantum Relief algorithm, is proposed. In the algorithm, all features of each sample are superposed by a certain quantum state through the CMP and rotation… 

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References

SHOWING 1-10 OF 26 REFERENCES
Training a Large Scale Classifier with the Quantum Adiabatic Algorithm
TLDR
This communication generalizes the baseline method to large scale classifier training and provides theoretical arguments as to why the proposed optimization method is superior to versions of boosting that only minimize the empirical loss.
Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning
TLDR
This work presents quantum algorithms for performing nearest-neighbor learning and k-means clustering and proves upper bounds on the number of queries to the input data required to compute such distances and find the nearest vector to a given test example.
A fast quantum mechanical algorithm for database search
TLDR
In early 1994, it was demonstrated that a quantum mechanical computer could efficiently solve a well-known problem for which there was no known efficient algorithm using classical computers, i.e. testing whether or not a given integer, N, is prime, in a time which is a finite power of o (logN) .
Quantum adiabatic machine learning
TLDR
This work applies and illustrates this approach to machine learning and anomaly detection via quantum adiabatic evolution in detail to the problem of software verification and validation, with a specific example of the learning phase applied to a problem of interest in flight control systems.
Computational Methods of Feature Selection
TLDR
This book discusses Supervised, Unsupervised, and Semi-Supervised Feature Selection Key Contributions and Organization of the Book Looking Ahead Unsuper supervised Feature Selection.
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
TLDR
This work reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF, an extension of RELIEF, as an estimator of attributes at each selection step for heuristic guidance of inductive learning algorithms.
Equivalences and Separations Between Quantum and Classical Learnability
TLDR
These results contrast known results that show that testing black-box functions for various properties, as opposed to learning, can require exponentially more classical queries than quantum queries.
Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
TLDR
Efficient randomized algorithms are given for factoring integers and finding discrete logarithms, two problems that are generally thought to be hard on classical computers and that have been used as the basis of several proposed cryptosystems.
...
1
2
3
...