A Convex Relaxation for Weakly Supervised Classifiers

This paper introduces a general multi-class approach to weakly supervised classification. Inferring the labels and learning the parameters of the model is usually done jointly through a block-coordinate descent algorithm such as expectation-maximization (EM), which may lead to local minima. To avoid this problem, we propose a cost function based on a convex relaxation of the soft-max loss. We then propose an algorithm specifically designed to efficiently solve the corresponding semidefinite program (SDP). Empirically, our method compares favorably to standard ones on different datasets for multiple instance learning and semi-supervised learning as well as on clustering tasks.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

A review of multi-instance learning assumptions

Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area

Multi-Instance Multi-Label Learning

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly.Comment: 64 pages, 10 figures; Artificial Intelligence, 201

A Comparison of Multi-instance Learning Algorithms

Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are specifically designed for MI problems whereas others have been upgraded or adapted from standard single-instance learning algorithms. Most algorithms have been evaluated on only one or two benchmark datasets, and there is a lack of systematic comparisons of MI learning algorithms. This thesis presents a comprehensive study of MI learning algorithms that aims to compare their performance and find a suitable way to properly address different MI problems. First, it briefly reviews the history of research on MI learning. Then it discusses five general classes of MI approaches that cover a total of 16 MI algorithms. After that, it presents empirical results for these algorithms that were obtained from 15 datasets which involve five different real-world application domains. Finally, some conclusions are drawn from these results: (1) applying suitable standard single-instance learners to MI problems can often generate the best result on the datasets that were tested, (2) algorithms exploiting the standard asymmetric MI assumption do not show significant advantages over approaches using the so-called collective assumption, and (3) different MI approaches are suitable for different application domains, and no MI algorithm works best on all MI problems

Learning from text and images: generative and discriminative models for partially labeled data

Image annotation is a challenging task of assigning keywords to an image given the content of an image. It has a variety of applications in multi-media data-mining and computer vision. Traditional machine learning approaches to image annotation require large amounts of labeled data. This requirement is often unrealistic, as obtaining labeled data is, in general, expensive and time consuming. However, large amounts of weakly labeled data and tagged images is readily available, in particular in the web and social network communities. In this thesis we address the problem of image annotation using weak supervision. In particular, we formulate the problem of image annotation as multiple instance multiple label learning problem and propose generative and discriminative models to tackle this learning problem. Multiple instance multiple label learning is a generalization of supervised learning in which the training examples are bags of instances and each bag is labeled with a set of labels. We explore two learning frameworks: generative and discriminative, and propose models within each framework to address the problem of assigning text keywords to images. The first approach, the generative model attempts to describe the process according to which the data was generated, and then learn its parameters from the data. This model is a non-parametric generalization of the known mixture model used in the past. We extend this model to a Hierarchical Dirichlet Process which allows for countably infinite mixture components. Our experimental evaluation shows that the performance of this model does not depend on the number of mixture components, unlike the standard mixture model which suffers from over-fitting for a large number of mixture components. The second approach is a discriminative model, which unlike generative model answers the following question: given the input bag of instances what is the most likely assignment of labels to the bag. We address this problem by learning as many classifiers as there are possible labels and force the classifiers to share weights using trace-norm regularization. We show that the performance of this model is comparable to the state-of-the-art multiple instance multiple label classifiers and that unlike some state-of-the-art models, it is scalable and practical for datasets with a large number of training instances and possible labels. Finally we generalize the discriminative model to a semi-supervised setting to allow the model take advantage of labeled and unlabeled data. We do so by assuming that the data lies in a low-dimensional manifold and introducing a penalty that enforces the classifiers assign similar labels to indirectly similar instances (i.e. instances that are near-by in the manifold space). The manifold is learned by constructing a similarity neighborhood graph over bags, and then graph-Laplacian is used to compute the penalty term

EEG representation using multi-instance framework on the manifold of symmetric positive definite matrices

The generalization and robustness of an electroencephalogram (EEG)-based system are crucial requirements in actual practices. To reach these goals, we propose a new EEG representation that provides a more realistic view of brain functionality by applying multi-instance (MI) framework to consider the non-stationarity of the EEG signal. In this representation, the non-stationarity of EEG is considered by describing the signal as a bag of relevant and irrelevant concepts. The concepts are provided by a robust representation of homogeneous segments of EEG signal using spatial covariance matrices. Due to the nonlinear geometry of the space of covariance matrices, we determine the boundaries of the homogeneous segments based on adaptive segmentation of the signal in a Riemannian framework. Each subject is described as a bag of covariance matrices of homogeneous segments and the bag-level discriminative information is used for classification. To evaluate the performance of the proposed approach, we examine it in a cultural neuroscience application for classification Iranian versus Swiss normal subjects to discover if strongly differing cultures can result in distinguishing patterns in brain electrical activity of the subjects. To confirm the effectiveness of the proposed representation, we also evaluate the proposed representation in EEG-based mental disorder diagnosis application for Attention Deficit Hyperactivity Disorder/Bipolar Mood Disorder, Schizophrenia/ normal, and Major Depression Disorder/normal diagnosis applications. Experimental results confirm the superiority of the proposed approach, which is gained due to the robustness of covariance descriptor, the effectiveness of Riemannian geometry, the benefits of considering the inherent nonstationary nature of the brain by applying bag-level discriminative information, and automatic handling the artifacts

Novel support vector machines for diverse learning paradigms

This dissertation introduces novel support vector machines (SVM) for the following traditional and non-traditional learning paradigms: Online classification, Multi-Target Regression, Multiple-Instance classification, and Data Stream classification. Three multi-target support vector regression (SVR) models are first presented. The first involves building independent, single-target SVR models for each target. The second builds an ensemble of randomly chained models using the first single-target method as a base model. The third calculates the targets\u27 correlations and forms a maximum correlation chain, which is used to build a single chained SVR model, improving the model\u27s prediction performance, while reducing computational complexity. Under the multi-instance paradigm, a novel SVM multiple-instance formulation and an algorithm with a bag-representative selector, named Multi-Instance Representative SVM (MIRSVM), are presented. The contribution trains the SVM based on bag-level information and is able to identify instances that highly impact classification, i.e. bag-representatives, for both positive and negative bags, while finding the optimal class separation hyperplane. Unlike other multi-instance SVM methods, this approach eliminates possible class imbalance issues by allowing both positive and negative bags to have at most one representative, which constitute as the most contributing instances to the model. Due to the shortcomings of current popular SVM solvers, especially in the context of large-scale learning, the third contribution presents a novel stochastic, i.e. online, learning algorithm for solving the L1-SVM problem in the primal domain, dubbed OnLine Learning Algorithm using Worst-Violators (OLLAWV). This algorithm, unlike other stochastic methods, provides a novel stopping criteria and eliminates the need for using a regularization term. It instead uses early stopping. Because of these characteristics, OLLAWV was proven to efficiently produce sparse models, while maintaining a competitive accuracy. OLLAWV\u27s online nature and success for traditional classification inspired its implementation, as well as its predecessor named OnLine Learning Algorithm - List 2 (OLLA-L2), under the batch data stream classification setting. Unlike other existing methods, these two algorithms were chosen because their properties are a natural remedy for the time and memory constraints that arise from the data stream problem. OLLA-L2\u27s low spacial complexity deals with memory constraints imposed by the data stream setting, and OLLAWV\u27s fast run time, early self-stopping capability, as well as the ability to produce sparse models, agrees with both memory and time constraints. The preliminary results for OLLAWV showed a superior performance to its predecessor and was chosen to be used in the final set of experiments against current popular data stream methods. Rigorous experimental studies and statistical analyses over various metrics and datasets were conducted in order to comprehensively compare the proposed solutions against modern, widely-used methods from all paradigms. The experimental studies and analyses confirm that the proposals achieve better performances and more scalable solutions than the methods compared, making them competitive in their respected fields

Una librería para el aprendizaje multi-instancia multi-etiqueta

Premio extraordinario de Trabajo Fin de Máster curso 2019/2020. Máster en Ingeniería InformáticaThis project presents a library to work on solving multi instance multi label classification problems. It describes the data format, the software architecture, as well as the different algorithmic proposals that it incorporates. The library allows to add new algorithms in a simple way, facilitating researchers in this area to develop, test and compare new proposals. In addition, it is free and open source and is implemented in Java, using the Weka and Mulan libraries. This way, users who work with these libraries in learning with multiple instances and in learning with multiple labels will find a familiar development environment.Este proyecto presenta una librería para trabajar en la resolución de problemas de clasificación con múltiples instancias y múltiples etiquetas. Se describe el formato de datos, la arquitectura software, así como las diferentes propuestas algorítmicas que incorpora. La librería permite añadir nuevos algoritmos de forma sencilla, facilitando a los investigadores en esta área el desarrollo, prueba y comparación de nuevas propuestas. Además, es libre y de código abierto y está implementada en Java, usando las librerías Weka y Mulan. De este modo, los usuarios habituados a trabajar en las librerías anteriores tanto en el aprendizaje con múltiples instancias como en el aprendizaje con múltiples etiquetas, respectivamente, se encontrarán con un entorno de desarrollo con el que están familiarizados