Hierarchical disjoint principal component analysis

Cavicchia, Carlo; Vichi, Maurizio; Zaccaria, Giorgia

doi:10.1007/s10182-022-00458-4

“…Quantum principal component analysis Principal component analysis is a typical dimension reduction algorithm [28]. It converts a group of variables that may have correlation into a group of linearly unrelated variables through orthogonal transformation, and the transformed group of variables is called principal component [29].…”

Section: Quantum Dimensionality Reductionmentioning

confidence: 99%

Robust Quantum Feature Selection Algorithm

Zhang¹,

Li²,

Xu³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

High dimensional data has been a notoriously challenging issue. Existing quantum dimension reduction technology mainly focuses on quantum principal component analysis. There are only a few works on the direction of quantum feature selection algorithm which they are not robust. Also, there are few quantum circuits designed for feature selection, in which some steps are not quantized yet. For example, existing quantum circuits cannot solve the objective function based on sparse learning. To deal with these issues, this paper proposes a robust quantum feature selection by designing a new quantum circuit. Specifically, the sparse regularization term and least squares loss are first applied to construct the proposed objective function. And then, six kinds of quantum registers and their initial states are prepared. In addition, quantum techniques, such as quantum phase estimation and controlled rotation, are employed to construct an alternating iterative quantum circuit to obtain the final quantum state of the feature selection variable. Finally, a series of experiments are conducted to verify that the proposed algorithm can accurately select important features and has good robustness.

show abstract

“…Quantum principal component analysis Principal component analysis is a typical dimension reduction algorithm [28]. It converts a group of variables that may have correlation into a group of linearly unrelated variables through orthogonal transformation, and the transformed group of variables is called principal component [29].…”

Section: Quantum Dimensionality Reductionmentioning

confidence: 99%

Robust Quantum Feature Selection Algorithm

Zhang¹,

Li²,

Xu³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

High dimensional data has been a notoriously challenging issue. Existing quantum dimension reduction technology mainly focuses on quantum principal component analysis. There are only a few works on the direction of quantum feature selection algorithm which they are not robust. Also, there are few quantum circuits designed for feature selection, in which some steps are not quantized yet. For example, existing quantum circuits cannot solve the objective function based on sparse learning. To deal with these issues, this paper proposes a robust quantum feature selection by designing a new quantum circuit. Specifically, the sparse regularization term and least squares loss are first applied to construct the proposed objective function. And then, six kinds of quantum registers and their initial states are prepared. In addition, quantum techniques, such as quantum phase estimation and controlled rotation, are employed to construct an alternating iterative quantum circuit to obtain the final quantum state of the feature selection variable. Finally, a series of experiments are conducted to verify that the proposed algorithm can accurately select important features and has good robustness.

show abstract