Semi-supervised learning through adaptive Laplacian graph trimming

The key problem of Graph-Based Semi-Supervised Learning (GBSSL) methods is how to construct the graph structure under some assumptions. While distance information among graph nodes is investigated well for graph construction, the density information is not given enough attention. In this paper, we propose a novel GBSSL method, named Density-Sensitive Manifold Learning (DSML), which introduces density distribution into graph construction by calculating a new propagation coefficient matrix. The experimental results show that DSML scheme performs better than traditional GBSSL methods. More importantly, the new propagation coefficient matrix can be easily introduced into traditional GBSSL methods to improve their performance, which is also validated in the experiments.

Graph based semi-supervised classification with probabilistic nearest neighbors

Graph-based semi-supervised learning (SSL) algorithms have been widely studied in the last few years. Most of these algorithms were designed from unconstrained optimization problems using a Laplacian regularizer term as smoothness functional in an attempt to reflect the intrinsic geometric structure of the data's marginal distribution. Although a number of recent research papers are still focusing on unconstrained methods for graph-based SSL, a recent statistical analysis showed that many of these algorithms may be unstable on transductive regression. Therefore, we focus on providing new constrained methods for graph-based SSL. We begin by analyzing the regularization framework of existing unconstrained methods. Then, we incorporate two normalization constraints into the optimization problem of three of these methods. We show that the proposed optimization problems have closed-form solution. By generalizing one of these constraints to any distribution, we provide generalized methods for constrained graph-based SSL. The proposed methods have a more flexible regularization framework than the corresponding unconstrained methods. More precisely, our methods can deal with any graph Laplacian and use higher order regularization, which is effective on general SSL taks. In order to show the effectiveness of the proposed methods, we provide comprehensive experimental analyses. Specifically, our experiments are subdivided into two parts. In the first part, we evaluate existing graph-based SSL algorithms on time series data to find their weaknesses. In the second part, we evaluate the proposed constrained methods against six state-of-the-art graph-based SSL algorithms on benchmark data sets. Since the widely used best case analysis may hide useful information concerning the SSL algorithms' performance with respect to parameter selection, we used recently proposed empirical evaluation models to evaluate our results. Our results show that our methods outperforms the competing methods on most parameter settings and graph construction methods. However, we found a few experimental settings in which our methods showed poor performance. In order to facilitate the reproduction of our results, the source codes, data sets, and experimental results are freely available.

Towards Dynamic Self-Training for Scalable Semi-Supervised Learning on Graphs

With the rapid growth of the Internet, more and more people interact with their friends in online social networks like Facebook1. Currently, the privacy issue of online social networks becomes a hot and dynamic research topic. Though some privacy protecting strategies are implemented, they are not stringent enough. Recently, Semi-Supervised Learning (SSL), which has the advantage of utilizing the unlabeled data to achieve better performance, attracts much attention from the web research community. By utilizing a large number of unlabeled data from websites, SSL can effectively infer hidden or sensitive information on the Internet. Furthermore, graph-based SSL is much more suitable for modeling real-world objects with graph characteristics, like online social networks. Thus, we propose a novel Community-based Graph (CG) SSL model that can be applied to exploit security issues in online social networks, then provide two consistent algorithms satisfying distinct needs. In order to evaluate the effectiveness of this model, we conduct a series of experiments on a synthetic data and two real-world data from StudiVZ2 and Facebook. Experimental results demonstrate that our approach can more accurately and confidently predict sensitive information of online users, comparing to previous models.

In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

Graph-based semi-supervised learning has recently come into focus for to its two defining phases: graph construction, which converts the data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. And the label inference is based on the smoothness assumption of semi-supervised learning. In this study, we propose an enhanced label inference approach which incorporates the importance of each vertex into the existing inference algorithms to improve the prediction capabilities of the algorithms. We also present extensions of three algorithms which are capable of taking the vertex importance variable to apply in learning. Experiments show that our algorithms perform better than the base algorithms on a variety of datasets, especially when the data is less smooth over the graphs.

Noise-robust semi-supervised learning via fast sparse coding

Graph-based semi-supervised learning plays an important role in large scale image classification tasks. However, the problem becomes very challenging in the presence of noisy labels and outliers. Moreover, traditional robust semi-supervised learning solutions suffers from prohibitive computational burdens thus cannot be computed for streaming data. Motivated by that, we present a novel unified framework robust structure-aware semi-supervised learning called Unified RSSL (URSSL) for batch processing and recursive processing robust to both outliers and noisy labels. Particularly, URSSL applies joint semi-supervised dimensionality reduction with robust estimators and network sparse regularization simultaneously on the graph Laplacian matrix iteratively to preserve the intrinsic graph structure and ensure robustness to the compound noise. First, in order to relieve the influence from outliers, a novel semi-supervised robust dimensionality reduction is applied relying on robust estimators to suppress outliers. Meanwhile, to tackle noisy labels, the denoised graph similarity information is encoded into the network regularization. Moreover, by identifying strong relevance of dimensionality reduction and network regularization in the context of robust semi-supervised learning (RSSL), a two-step alternative optimization is derived to compute optimal solutions with guaranteed convergence. We further derive our framework to adapt to large scale semi-supervised learning particularly suitable for large scale image classification and demonstrate the model robustness under different adversarial attacks. For recursive processing, we rely on reparameterization to transform the formulation to unlock the challenging problem of robust streaming-based semi-supervised learning. Last but not least, we extend our solution into distributed solutions to resolve the challenging issue of distributed robust semi-supervised learning when images are captured by multiple cameras at different locations. Extensive experimental results demonstrate the promising performance of this framework when applied to multiple benchmark datasets with respect to state-of-the-art approaches for important applications in the areas of image classification and spam data analysis.

The key points of the semi-supervised learning problem are the label smoothness and cluster assumptions. In graph-based semi-supervised learning, graph representations of the data are so important that different graph representations can affect the classification results heavily. We present a novel method to produce a graph called smooth Markov random walk graph which takes into account the two assumptions employed by semi-supervised learning. The new graph is achieved by modifying the eigenspectrum of the transition matrix of Markov random walk graph and is sufficiently smooth with respect to the intrinsic structure of labeled and unlabeled points.We believe the smoother graph will benefit semi-supervised learning. Experiments on artificial and real world dataset indicate that our method provides superior classification accuracy over several state-of-the-art methods.

Hardware of Distributed WSNs is limited, which turns into a big challenge. In recent years, there are some pattern-based query strategies have been proposed. But compared to the methods based on machine learning, these methods need high energy consumption in the calculation of distance. In this paper, we propose a pattern-based query strategy based on semi-supervised machine learning in wireless sensor network. The strategy uses historical time series data streams for o†ine machine learning. First of all, we flnd out the flxed length subsequences and screen out the suitable data sequences as training dataset and sample dataset. And then, because labeled data is less in practical application, we do label propagation with graph-based semi-supervised machine learning algorithm, Local and Global Consistency (LGC), to increase the number of labeled data sequences. Next, we use the supervised learning algorithm Support Vector Machine (SVM) to train a classifler. Finally, for a new data sequence, we can directly get its category through the classifler. Experimental results show that the performance of our strategy is almost better than other pattern-based query strategies on classiflcation accuracy and energy consumption.

Speaker attribution is required in many real-world applications, such as\nmeeting transcription, where speaker identity is assigned to each utterance\naccording to speaker voice profiles. In this paper, we propose to solve the\nspeaker attribution problem by using graph-based semi-supervised learning\nmethods. A graph of speech segments is built for each session, on which\nsegments from voice profiles are represented by labeled nodes while segments\nfrom test utterances are unlabeled nodes. The weight of edges between nodes is\nevaluated by the similarities between the pretrained speaker embeddings of\nspeech segments. Speaker attribution then becomes a semi-supervised learning\nproblem on graphs, on which two graph-based methods are applied: label\npropagation (LP) and graph neural networks (GNNs). The proposed approaches are\nable to utilize the structural information of the graph to improve speaker\nattribution performance. Experimental results on real meeting data show that\nthe graph based approaches reduce speaker attribution error by up to 68%\ncompared to a baseline speaker identification approach that processes each\nutterance independently.\n

The recent years have witnessed a surge of interests in graph-based semi-supervised learning (GBSSL). In this paper, we will introduce a series of works done by our group on this topic including: 1) a method called linear neighborhood propagation (LNP) which can automatically construct the optimal graph; 2) a novel multilevel scheme to make our algorithm scalable for large data sets; 3) a generalized point charge scheme for GBSSL; 4) a multilabel GBSSL method by solving a Sylvester equation; 5) an information fusion framework for GBSSL; and 6) an application of GBSSL on fMRI image segmentation.

Semi-supervised matrixized least squares support vector machine

Symmetric low-rank representation with adaptive distance penalty for semi-supervised learning