Shape Clustering as a Type of Procrustes Analysis

Iwata, Koichiro

doi:10.1007/978-3-030-04212-7_19

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…Where = trace( ′ ) trace( ′ ) [18]. The full Procrustes mean (FPM) is a technique for getting the mean of configuration matrices of similar shapes [26]. FPM does not give a measure of the match, so this algorithm abandons in here.…”

Section: A Brief History Of Procrustes Analysismentioning

confidence: 99%

“…Then, Procrustes had been developed into the Full Procrustes Mean (FPM) [18] and the Goodness-of-fit of Procrustes (GoFP) [19]. Procrustes has recently been implemented in several researchers, namely to determine variables selection [20], measure the quality of biplot analysis [21][22], measure the quality of imputation data [23] [24], detect outliers [25], and solve shape clustering problem [26]. Based on the result, Procrustes has a potential to tackle the misclassification problems when the outliers are assumed as the misclassified objects.…”

Section: Introductionmentioning

confidence: 99%

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Classification Based on Configuration Objects by Using Procrustes Analysis

Ananda¹,

Prasetiadi²

2021

J.INFOTEL

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Classification is one of the data mining topics that will predict an object to go into a certain group. The prediction process can be performed by using similarity measures, classification trees, or regression. On the other hand, Procrustes refers to a technique of matching two configurations that have been implemented for outlier detection. Based on the result, Procrustes has a potential to tackle the misclassification problem when the outliers are assumed as the misclassified object. Therefore, the Procrustes classification algorithm (PrCA) and Procrustes nearest neighbor classification algorithm (PNNCA) were proposed in this paper. The results of those algorithms had been compared to the classical classification algorithms, namely k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), AdaBoost (AB), Random Forest (RF), Logistic Regression (LR), and Ridge Regression (RR). The data used were iris, cancer, liver, seeds, and wine dataset. The minimum and maximum accuracy values obtained by the PrCA algorithm were 0.610 and 0.925, while the PNNCA were 0.610 and 0.963. PrCA was generally better than k-NN, SVM, and AB. Meanwhile, PNNCA was generally better than k-NN, SVM, AB, and RF. Based on the results, PrCA and PNNCA certainly deserve to be proposed as a new approach in the classification process.

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Section: A Brief History Of Procrustes Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification Based on Configuration Objects by Using Procrustes Analysis

Ananda¹,

Prasetiadi²

2021

J.INFOTEL

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“…We have mentioned that a shape is expressed as a configuration matrix, a similarity transformation invariant distance between shapes can be given by the OSS, and a rotation and translation invariant distance can be given by the PSS. Shape clustering [12], [13] is an interesting application of their shape distances because clustering can only be performed if we can define a suitable distance for shapes. Now we briefly introduce a stochastic view of the generation of the configuration matrix.…”

Section: Shape Clusteringmentioning

confidence: 99%

“…In the following, we use O-notation; refer to [14] for details. In fact, the computation of the OSS in (7) is decomposed into three operations: matrix centering, such as CX 1 ; matrix product-and-trace, such as CX 1 ; and singular value decomposition (SVD) on the right-hand side of (12). Assume that ≥ m, without loss of generality.…”

Section: I K ) Denote the Indices Of The K-nns Return Y End Functionmentioning

confidence: 99%

Revisiting a Nearest Neighbor Method for Shape Classification

Iwata

2020

IEICE Trans. Inf. & Syst.

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The nearest neighbor method is a simple and flexible scheme for the classification of data points in a vector space. It predicts a class label of an unseen data point using a majority rule for the labels of known data points inside a neighborhood of the unseen data point. Because it sometimes achieves good performance even for complicated problems, several derivatives of it have been studied. Among them, the discriminant adaptive nearest neighbor method is particularly worth revisiting to demonstrate its application. The main idea of this method is to adjust the neighbor metric of an unseen data point to the set of known data points before label prediction. It often improves the prediction, provided the neighbor metric is adjusted well. For statistical shape analysis, shape classification attracts attention because it is a vital topic in shape analysis. However, because a shape is generally expressed as a matrix, it is non-trivial to apply the discriminant adaptive nearest neighbor method to shape classification. Thus, in this study, we develop the discriminant adaptive nearest neighbor method to make it slightly more useful in shape classification. To achieve this development, a mixture model and optimization algorithm for shape clustering are incorporated into the method. Furthermore, we describe several helpful techniques for the initial guess of the model parameters in the optimization algorithm. Using several shape datasets, we demonstrated that our method is successful for shape classification.

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Shape Clustering as a Type of Procrustes Analysis

Cited by 2 publications

References 9 publications

Classification Based on Configuration Objects by Using Procrustes Analysis

Classification Based on Configuration Objects by Using Procrustes Analysis

Revisiting a Nearest Neighbor Method for Shape Classification

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