Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification

Abstract

The graph-based semi-supervised label propagation algorithm has delivered impressive classification results. However, the estimated soft labels typically contain mixed signs and noise, which cause inaccurate predictions due to the lack of suitable constraints. Moreover, available methods typically calculate the weights and estimate the labels in the original input space, which typically contains noise and corruption. Thus, the encoded similarities and manifold smoothness may be inaccurate for label estimation. In this paper, we present effective schemes for resolving these issues and propose a novel and robust semi-supervised classification algorithm, namely, the triple-matrix-recovery-based robust auto-weighted label propagation framework (ALP-TMR). Our ALP-TMR introduces a triple matrix recovery mechanism to remove noise or mixed signs from the estimated soft labels and improve the robustness to noise and outliers in the steps of assigning weights and predicting the labels simultaneously. Our method can jointly recover the underlying clean data, clean labels and clean weighting spaces by decomposing the original data, predicted soft labels or weights into a clean part plus an error part by fitting noise. In addition, ALP-TMR integrates the auto-weighting process by minimizing reconstruction errors over the recovered clean data and clean soft labels, which can encode the weights more accurately to improve both data representation and classification. By classifying samples in the recovered clean label and weight spaces, one can potentially improve the label prediction results. The results of extensive experiments demonstrated the satisfactory performance of our ALP-TMR.

ODMTCNet: An Interpretable Multi-view Deep Neural Network Architecture for Image Feature Representation

“…In addition, ODMTCNet achieves better performance than that of state-of-the-art algorithms. Furthermore, it is observed that ODMTCNet obtains higher recognition accuracy than the algorithms in [40,41,42] even with less training samples, validating its effectiveness on image feature representation. Again, the number of filters L i (i=1, 2) associated with the optimal performance is 8 (e.g., L 1 = L 2 = 8) as given in TABLE 5, less than the number of classes 80.…”

Transductive Nonnegative Matrix Tri-Factorization

“…In [54], a novel adaptive transductive label propagation approach was proposed by joint discriminative clustering on manifolds for representing and classifying high-dimensional data. In [55], to acquire a more accurate prediction in classification, the triple matrix recovery-based robust auto-weighted label propagation framework (ALP-TMR) was proposed by introducing a TMR mechanism to remove noise or mixed signs from the estimated soft labels and improve the robustness to noise and outliers in the steps of assigning weights and predicting the labels simultaneously.…”

A Novel Method for Predicting Fault Labels of Roller Bearing by Generalized Laplacian Matrix

“…Next, we will verify the label propagation ability of the GLLP method through experiments, and make the results more intuitively observed by everyone through the visualization method. In order to fully prove the superior performance of the proposed GLLP method, five latest graph-based label propagation algorithms are selected as baselines for comparison: ALP [27] (autoweighted label propagation):, BPFLP [28](belief-peaks clustering based on fuzzy label propagation), GLP [29](graph layout based label propagation), LNLP [30](linear neighborhood label propagation) and NLPPC [31] of our proposed GLLP method is the highest. In the process of label propagation, ALP and BPFLP propagates negative labels to positive labels, which is over propagation.…”