Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices

• URL: http://arxiv.org/abs/2102.00667v1
• Date: Mon, 1 Feb 2021 06:58:39 GMT
• Title: Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices
• Authors: Fengzhen Tang, Haifeng Feng, Peter Tino, Bailu Si, Daxiong Ji
• Abstract summary: We develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In this paper, we generalize the probabilistic learning vector quantization algorithm for data points living on the manifold of symmetric positive definite matrices. Empirical investigations on synthetic data, image data, and motor imagery EEG data demonstrate the superior performance of the proposed method.
• Score: 3.727361969017079
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matrices, which are inherently points that live on a curved Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, traditional Euclidean machine learning algorithms yield poor results on such data. In this paper, we generalize the probabilistic learning vector quantization algorithm for data points living on the manifold of symmetric positive definite matrices equipped with Riemannian natural metric (affine-invariant metric). By exploiting the induced Riemannian distance, we derive the probabilistic learning Riemannian space quantization algorithm, obtaining the learning rule through Riemannian gradient descent. Empirical investigations on synthetic data, image data , and motor imagery EEG data demonstrate the superior performance of the proposed method.

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