Graph-Based Semi-Supervised Learning

Subramanya, Amarnag; Talukdar, Partha

doi:10.2200/s00590ed1v01y201408aim029

Cited by 98 publications

(78 citation statements)

References 65 publications

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“…To perform SRKDA in a semi-supervised way, one straightforward solution is to use the label information to guide the construction of the affinity matrix W, as in [57][58][59]. Let G = (V, E) be a graph with set of vertices V, which is connected by a set of edges E. The vertices of the graph are the labeled and unlabeled instances x S j , y S…”

Section: Semi-supervised Versionmentioning

confidence: 99%

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Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification

et al. 2017

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Abstract:In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous domain adaptation problems, i.e., situations in which the data to be processed and recognized are collected from different heterogeneous domains. Specifically, the multi-view canonical correlation analysis scheme is utilized to extract multiple correlation subspaces that are useful for joint representations for data association across domains. This scheme makes homogeneous domain adaption algorithms suitable for heterogeneous domain adaptation problems. Additionally, inspired by fusion methods such as Ensemble Learning (EL), this work proposes a weighted voting scheme based on canonical correlation coefficients to combine classification results in multiple correlation subspaces. Finally, the semi-supervised MVCCAE extends the original procedure by incorporating multiple speed-up spectral regression kernel discriminant analysis (SRKDA). To validate the performances of the proposed supervised procedure, a single-view canonical analysis (SVCCA) with the same base classifier (Random Forests) is used. Similarly, to evaluate the performance of the semi-supervised approach, a comparison is made with other techniques such as Logistic label propagation (LLP) and the Laplacian support vector machine (LapSVM). All of the approaches are tested on two real hyperspectral images, which are considered the target domain, with a classifier trained from synthetic low-dimensional multispectral images, which are considered the original source domain. The experimental results confirm that multi-view canonical correlation can overcome the limitations of SVCCA. Both of the proposed procedures outperform the ones used in the comparison with respect to not only the classification accuracy but also the computational efficiency. Moreover, this research shows that canonical correlation weighted voting (CCWV) is a valid option with respect to other ensemble schemes and that because of their ability to balance diversity and accuracy, canonical views extracted using partially joint random view generation are more effective than those obtained by exploiting disjoint random view generation.

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Section: Semi-supervised Versionmentioning

confidence: 99%

“…where D denotes a diagonal matrix defined by D ii = ∑ j W i,j (see [59,60] (Chapter 5) for more details on different families of graph based SSL methods). According to this procedure, and inserting the notation for DA using multiple view CCA, the new semi-supervised procedure follows the steps reported in Algorithm 2.…”

Section: Semi-supervised Versionmentioning

confidence: 99%

Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification

et al. 2017

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“…However, preparing labelled data for supervised learning is expensive, even if annotated via expert-sourcing (Chapter 3), because the volume of unlabelled data is becoming unmanageable [83]. Therefore, some recent research studies have proposed the adoption of semi-supervised learning (SSL) algorithms.…”

Section: Optimal Graph-based Learning For Automatic Annotation Of Animentioning

confidence: 99%

“…Instead of learning from a large amount of labelled data, SSL algorithms learn from both labelled and unlabelled data [83]. In addition, some previous research efforts have proved that graphs provide a natural way to represent data in a variety of domains [83].…”

Section: Optimal Graph-based Learning For Automatic Annotation Of Animentioning

confidence: 99%

“…Instead of learning from large amount of labelled data, Semi-Supervised machine Learning (SSL) algorithms learn both from labelled and unlabelled data [83]. Also, previous research has shown that graphs provide a natural way to represent data in a variety of domains [83], thus graph-based SSL algorithms have been successfully used to extract class-instance pairs from large labelled and unlabelled data sets for human activity behaviour recognition [84,85].…”

Section: Overviewmentioning

confidence: 99%

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Semantic annotation and reasoning for sensor data streams

Gao¹

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School of Information Technology and Electrical Engineeringii Abstract Sensor technologies and wireless sensor networks are enabling the capture and storage of large volumes of sensor data streams. However there are a number of characteristics associated with sensor data streams that hinder the sharing, analysis and re-use of such data on the Web. For example, because sensor data is both temporal and spatial in nature, its multi-dimensionality, combined with variations in granularity, makes it more difficult to analyse and interpret. These issues have created major challenges associated with the management, representation, analysis and indexing of large volumes of sensor data streams. Consequently, there is an urgent need to markup sensor data streams with well-defined semantics to drive the development of advanced applications such as situation awareness, predictive models and event detection. Given well-structured and semantically annotated sensor data streams, it is possible to reason across annotated sensor data streams to deduce new or implicit knowledge, discover significant (and erroneous) data and events and answer complex queries.This thesis focuses on the application area of ecosystem monitoring. As such, it investigates novel solutions to the semantic annotation and reasoning challenges associated with sensor data streams acquired by ecosystem scientists who are monitoring: a) species behaviour and b) micro-climate changes within environmentally-sensitive regions. Within this context, this thesis focuses on the design, implementation and evaluation of innovative methods to tackle different challenges associated with the semantic annotation and reasoning of two classes of sensor data: a) animal accelerometry data streams (acquired via animal-attached tri-axial accelerometers); and b) environmental sensor data streams (acquired from wireless sensor networks). These two categories of sensor data are of particular interest because they are rapidly growing in volume, they present different but similar challenges and there is a need to correlate them in order to determine if changes in the environment are impacting on species behaviour.The first component of the thesis investigates optimum methods of combining domain expert annotations and machine learning to improve the precision and efficiency of semantic annotations on 3D accelerometry data streams (to support animal behaviour recognition and analysis). The second component seeks to minimize the cost and effort involved in developing training corpuses for machine learning approaches, by evaluating an Optimal Graph Learning approach to automatic semantic annotation of 3D accelerometry data streams. The third component of the thesis tackles the problem of detecting, annotating and filtering errors and outliers in sensor data streams, from wireless sensor networks, employed for environmental monitoring. The fourth and final component investigates, implements and evaluates an approach for reasoning across multiple environmental sensor data streams to infer h...

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Unsupervised and Semisupervised Learning

Pisztora

2021

Wiley StatsRef: Statistics Reference Online

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Methodologies for unsupervised and semisupervised learning are reviewed. For unsupervised learning, or clustering, the focus is on mixture‐model‐based approaches under both the classic and mode association frameworks. High‐dimensional data pose a major challenge for clustering. We thus discuss in detail variable selection and the hidden Markov model on variable blocks, which exploits a graph structure to simplify the dependence among variables. We also present topics that emerged relatively recently such as clustering distributional data under the Wasserstein metric and uncertainty assessment for cluster analysis. Semisupervised learning has attracted growing interest in the machine learning community in recent years. We review foundational approaches including self‐training, semisupervised generative models, and graphical models. We then describe in greater depth entropy minimization, consistency regularization, and mixup augmentation, methods that are utilized in state‐of‐the‐art models such as MixMatch.

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Graph-Based Semi-Supervised Learning

Cited by 98 publications

References 65 publications

Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification

Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification

Semantic annotation and reasoning for sensor data streams

Unsupervised and Semisupervised Learning

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