# Low-Rank Approximation of Difference between Correlation Matrices Using Inner Product

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## Abstract

**:**

**Methods:**Our proposed dimensional reduction clustering approach consists of low-rank approximation and a clustering algorithm. The low-rank matrix, which reflects the difference, is estimated from the inner product of the difference matrix, not only from the difference. In addition, the low-rank matrix is calculated based on the majorize–minimization (MM) algorithm such that the difference is bounded within the range $-1$ to 1. For the clustering process, ordinal k-means is applied to the estimated low-rank matrix, which emphasizes the clustering structure.

**Results:**Numerical simulations show that, compared with other approaches that are based only on differences, the proposed method provides superior performance in recovering the true clustering structure. Moreover, as demonstrated through a real-data example of brain activity measured via fMRI during the performance of a working memory task, the proposed method can visually provide interpretable community structures consisting of well-known brain functional networks, which can be associated with the human working memory system.

**Conclusions:**The proposed dimensional reduction clustering approach is a very useful tool for revealing and interpreting the differences between correlation matrices, even when the true differences tend to be relatively small.

## 1. Introduction

## 2. Methods

#### 2.1. Model of Proposed Method

#### 2.2. Formulation of Proposed Method

#### 2.3. Algorithm for Estimating Low-Rank Correlation Matrix Based on MM Algorithm

Algorithm 1 Estimating correlation matrix with rank d |

Require: Inner product $\mathsf{\Delta}$, rank d, initial vectors ${\mathit{z}}_{i}\phantom{\rule{0.277778em}{0ex}}(i=1,2,\cdots ,p)$, and $\epsilon >0$Ensure: coordinate matrix $\mathit{X}$ with rank d$t\leftarrow 0$ while ${L}^{\left(t\right)}\left(\mathit{X}\right|\mathsf{\Delta})-{L}^{(t-1)}\left(\mathit{X}\right|\mathsf{\Delta})\ge \epsilon $ dofor $i=1,2,\cdots ,p$ do${\mathit{x}}_{i}\leftarrow \left(\right)open="("\; close=")">{\sum}_{j\ne i}{\delta}_{ij}\parallel {\mathit{d}}_{i}\parallel \phantom{\rule{0.277778em}{0ex}}\parallel {\mathit{d}}_{j}\parallel \phantom{\rule{0.277778em}{0ex}}{\mathit{x}}_{j}-({\mathit{B}}_{i}-{\lambda}_{i}{\mathit{I}}_{d}){\mathit{z}}_{i}$ ${\mathit{z}}_{i}\leftarrow {\mathit{x}}_{i}$ end for$t\leftarrow t+1$ end whilereturn$\mathit{X}$ |

#### 2.4. Simulation Study

**Factor****1:****Methods**

**Factor****2:****Rank**

**Factor****3:****Number of clusters**

**Factor****4:****Difference between true correlations**

#### 2.5. fMRI Data for Mental Arithmetic Task

#### 2.5.1. Participants

#### 2.5.2. Experimental Design

#### 2.5.3. Data Acquisition

#### 2.5.4. Data Preprocessing

#### 2.5.5. Functional Connectivity Analysis: Derivation of Correlation Matrices

## 3. Results

#### 3.1. Simulation Results

#### 3.2. Results of fMRI Data Analysis

## 4. Conclusions and Remarks

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AAL | automated anatomical labeling |

ARI | adjusted Rand index |

aCompCor | an anatomical component-based noise correction method |

BOLD | blood-oxygen-level-dependent |

CON | cingulo-opercular network |

DAN | dorsal attention network |

DMN | default mode network |

EEG | electroencephalography |

fMRI | functional magnetic resonance imaging |

fNIRS | functional near-infrared spectroscopy |

FPN | fronto-parietal network |

IQRs | interquartile ranges |

MNI | Montreal Neurological Institute |

ROIs | regions of interest |

SN | salience network |

VN | visual network |

TPN | task positive network |

VAN | ventral attention network |

WM | working memory |

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**Figure 3.**Simulation result for ${\sigma}_{ij}^{\left(s\right)}=0.1\phantom{\rule{0.277778em}{0ex}}(s=1,2,3;\phantom{\rule{0.277778em}{0ex}}i\ne j)$.

**Figure 4.**Simulation result for ${\sigma}_{ij}^{\left(s\right)}=0.2\phantom{\rule{0.277778em}{0ex}}(s=1,2,3;\phantom{\rule{0.277778em}{0ex}}i\ne j)$.

**Figure 5.**Change in rate for objective function; vertical and horizontal axes indicate rank and ratio of values for objective function, respectively.

**Figure 6.**Estimated correlation matrix for $d=3$ and clustering result; upper-left figure of each heatmap indicates histograms of these coefficients.

Names of Factors | Levels | Descriptions |
---|---|---|

Methods | 4 | proposal, Method 2, Method 3, and Method 4 |

Rank | 3 | Rank $=2,3$ and 4 |

The number of clusters | 3 | $k=2,3$, and 4 |

The difference between true correlation | 2 | $0.1$, and $0.2$ |

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Tanioka, K.; Hiwa, S.
Low-Rank Approximation of Difference between Correlation Matrices Using Inner Product. *Appl. Sci.* **2021**, *11*, 4582.
https://doi.org/10.3390/app11104582

**AMA Style**

Tanioka K, Hiwa S.
Low-Rank Approximation of Difference between Correlation Matrices Using Inner Product. *Applied Sciences*. 2021; 11(10):4582.
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**Chicago/Turabian Style**

Tanioka, Kensuke, and Satoru Hiwa.
2021. "Low-Rank Approximation of Difference between Correlation Matrices Using Inner Product" *Applied Sciences* 11, no. 10: 4582.
https://doi.org/10.3390/app11104582