There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion has recently been introduced as a method for transductive (semi-supervised) multi-label classification, and has seve…
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48 results for “view label classification”
S-multi-SNE combines t-SNE with labelling data for better multi-view data visualisation and classification.
problem Visualizing and classifying multi-view data with limited labels.
method S-multi-SNE extends multi-SNE by incorporating labelling data as an additional view for semi-supervised classification and visualization.
result Including labelling information significantly improves the projection of samples and classification performance.
To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying late…
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currentl…
Competitive methods for multi-label classification typically invest in learning labels together. To do so in a beneficial way, analysis of label dependence is often seen as a fundamental step, separate and prior to constructing a classifier. Some methods invest up to hundreds of times more computational effort in build…
In many machine learning problems, labeled training data is limited but unlabeled data is ample. Some of these problems have instances that can be factored into multiple views, each of which is nearly sufficent in determining the correct labels. In this paper we present a new algorithm for probabilistic multi-view lear…
Unified framework for multi-view learning with orthogonal projections.
problem Learning individual orthogonal projections for multiple views.
method Successive approximations via eigenvectors, iterative Krylov subspace method.
result Consistently competitive and often better than existing methods.
Develops gradient boosting for multi-label classification.
problem Lack of customizable learning algorithms for multi-label classification.
method Generalizes gradient boosting to multi-output problems and proposes an algorithm for learning multi-label classification rules.
result Ability to minimize both decomposable and non-decomposable loss functions.
Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views. Existing methods mainly focus on fusing multi-view information at an early stage, e.g., by learning a common feature subspace among multiple views. However, these early fusio…
A new framework decouples SSL tasks into VDA and VLC, revealing VDA's importance.
problem Designing effective self-supervised learning tasks without manual annotation.
method Borrowing a multi-view perspective, the paper decouples popular pretext tasks into VDA and VLC, focusing on VDA's role in feature learning.
result VDA tasks dominate SSL performance, and integrating predictions from augmented views improves overall performance.
The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. Datasets in extreme classification exhibit a long tail of labels which have small number of positive training instances. In this work, w…
End-to-end deep metric learning tackles multi-label image classification.
problem Multi-label image classification problem.
method Two-way deep distance metric learning in a latent space with a reconstruction module.
result Our method outperforms state-of-the-arts on publicly available image datasets.
This paper develops convex surrogates for optimizing the multi-label F-measure.
problem Optimizing the F-measure for multi-label classification is computationally hard.
method Designing convex surrogate losses calibrated for the F-measure.
result The F-measure for multi-label problems has a rank of at most .
Framework for multi-view redescription mining overcomes limitations of existing approaches.
problem Challenges in revealing non-trivial associations between different subsets of attributes (views).
method Memory-efficient, extensible framework using multi-target regression or multi-label classification algorithms.
result Framework can generate redescriptions from multiple views, improving over existing two-view approaches.
Multi-view data, that is matched sets of measurements on the same subjects, have become increasingly common with advances in multi-omics technology. Often, it is of interest to find associations between the views that are related to the intrinsic class memberships. Existing association methods cannot directly incorpora…
The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of classification of sequential data. If labels indices are considered as time indices, the problems can often be seen as equivalent. In this pape…
Study improves deep learning chest X-ray models by incorporating lateral views.
problem Lack of lateral views in training datasets limits deep learning performance.
method Used PadChest dataset with multiple views to explore merging methods.
result Incorporating lateral views increases model performance for 32 labels.
Mixup helps learn both features in multi-view classification problems.
problem Learning both features in multi-view classification problems.
method Training with a specific instantiation of Mixup.
result Training with Mixup leads to learning both features for every class.
This research tackles multiclass classification by introducing a method for label ranking.
problem The challenge is to predict a sorted list of labels based on input features.
method Inspired by ranking median regression, the One-Versus-One (OVO) approach is used to achieve optimal ranking.
result The OVO approach yields an optimal ranking of labels with overwhelming probability under noise conditions.
A Bayesian Boosting Modelstat.ML
We offer a novel view of AdaBoost in a statistical setting. We propose a Bayesian model for binary classification in which label noise is modeled hierarchically. Using variational inference to optimize a dynamic evidence lower bound, we derive a new boosting-like algorithm called VIBoost. We show its close connections …
Extends information bottleneck to multi-view unsupervised learning.
problem Identifying superfluous information in unlabeled multi-view data.
method Multi-view information bottleneck model, leveraging data augmentation.
result State-of-the-art results on Sketchy and MIR-Flickr datasets.
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied. We introduce a semi-supervised neural n…
Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use of class labels. Recent CCA methods have started to address this weakness but are limited in that they do not simultaneously optimize the CC…
Label aggregation makes learning robust to noisy labels.
problem Learning from noisy labels.
method Label aggregation and risk consistency.
result Aggregated labels lead to stronger consistency guarantees.
In Natural Language Processing (NLP) tasks, data often has the following two properties: First, data can be chopped into multi-views which has been successfully used for dimension reduction purposes. For example, in topic classification, every paper can be chopped into the title, the main text and the references. Howev…
In emotion recognition, it is difficult to recognize human's emotional states using just a single modality. Besides, the annotation of physiological emotional data is particularly expensive. These two aspects make the building of effective emotion recognition model challenging. In this paper, we first build a multi-vie…
M4L-JMF tackles multi-typed objects learning, improving on M3L.
problem Learning from multi-typed objects with diverse features and labels.
method Joint matrix factorization to encode and factorize multi-typed bags and their instances.
result M4L-JMF outperforms existing methods on benchmark datasets.
Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning. The underlying premises are that there exist correlations among input parts and among output targets, and the …
Paper proposes MMVFL for multi-class VFL with multiple participants.
problem Privacy-preserving multi-class VFL with multiple participants.
method Extends multi-view learning to enable label sharing among multiple VFL participants.
result MMVFL effectively shares label information among multiple VFL participants and matches multi-class classification performance.
Modular deep learning framework using pairwise labels without backpropagation.
problem Efficiently training deep neural networks with limited supervision.
method Stacked linear models in feature spaces, provably optimal modular learning framework.
result High accuracy (94.88%) achieved with minimal labeled examples (1 per class).
End-to-end deep learning for multi-view clustering improves accuracy across various data types.
problem Limited multi-view clustering methods for general data types and suboptimal two-stage process.
method Permutation-based canonical correlation objective for fused representations; pseudo-labels for clustering; theoretical error bound.
result Proposed model provides meaningful fused representations and effective clustering across multiple views.
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which …
Machine learning has played an important role in information retrieval (IR) in recent times. In search engines, for example, query keywords are accepted and documents are returned in order of relevance to the given query; this can be cast as a multi-label ranking problem in machine learning. Generally, the number of ca…
New method makes machine learning models robust to label flipping attacks.
problem Machine learning models are vulnerable to label flipping attacks.
method Randomized smoothing over arbitrary functions to build certifiably robust classifiers.
result Linear classifiers are robust to label flipping attacks with deterministic bounds.
Unified view of label shift estimation methods.
problem Label distribution changes but class-conditional distributions remain the same.
method Unified view of two approaches: BBSE and MLLS.
result Unified framework and theoretical characterization of MLLS.
Graph representations have increasingly grown in popularity during the last years. Existing representation learning approaches explicitly encode network structure. Despite their good performance in downstream processes (e.g., node classification, link prediction), there is still room for improvement in different aspect…
We investigate the effects of the unsupervised pre-training method under the perspective of information theory. If the input distribution displays multiple views of the supervision, then unsupervised pre-training allows to learn hierarchical representation which communicates these views across layers, while disentangli…
Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance has further increased recently due to the growing need for large-scale datasets to train deep learning models. Weak or noisy supervision could originate from multiple sources i…
StaPLR improves Alzheimer's disease classification by identifying important MRI scan types and measures.
problem Classifying Alzheimer's disease using multi-source MRI data.
method Stacked penalized logistic regression (StaPLR) with hierarchical multi-view structure and new view importance measure.
result StaPLR identifies the most important MRI scan types and measures for Alzheimer's disease classification.
CuBAS selects informative data points based on curvature for better classification.
problem Lack of efficient sampling strategies for maximizing dataset informativeness.
method Information-geometric framework using curvature scores to select labeled data.
result Consistent and statistically significant improvements over random and uncertainty-based sampling.
Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples. For domains without such data, an attractive alternative is to train models with light, or distant supervision. In this paper, we…
New methods lift weak supervision to structured prediction, providing robustness guarantees.
problem Applying weak supervision techniques to structured prediction problems.
method Introducing pseudo-Euclidean embeddings, tensor decompositions, and invariants for consistent noise rate estimation.
result Generalization guarantees nearly identical to those for models trained on clean data.
According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous - that is, i…
New approach for classification using trigonometric polynomial kernels from signal processing.
problem Classifying objects in arbitrary compact metric spaces.
method Localized trigonometric polynomial kernels for separating different probability measures.
result Minimal number of queried labels for perfect classification.
New neural network approach using mutual information.
problem Training neural networks for imbalanced datasets.
method Converts neural network classifiers to mutual information evaluators.
result New form of softmax leads to better classification accuracy, especially for imbalanced datasets.
We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multi-view subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications,…
In this paper we propose a strategy for semi-supervised image classification that leverages unsupervised representation learning and co-training. The strategy, that is called CURL from Co-trained Unsupervised Representation Learning, iteratively builds two classifiers on two different views of the data. The two views c…
C-HMCNN(h) improves HMC classification by leveraging class hierarchy.
problem Hierarchical multi-label classification with class hierarchy constraints.
method Exploits class hierarchy to produce coherent predictions for multi-label classification.
result C-HMCNN(h) outperforms state-of-the-art models in HMC classification.