A novel GAN approach for multiview learning with missing data.
problem Learning from multiview data with missing observations.
method Biconditional GAN with two generators and a discriminator for conditional generation of missing views.
result The discriminator achieves significant classification performance and generators learn missing views without external resources.
CLIM-FS tackles mixed-missing multi-view unsupervised feature selection.
problem Mixed-missing multi-view data with incomplete features and views.
method Integrates imputation of missing views and variables into feature selection model based on nonnegative orthogonal matrix factorization.
result CLIM-FS outperforms state-of-the-art methods on real-world datasets.
Proposes CVKT to complete missing kernel matrices across multiple views.
problem Missing data in multiple views of kernel matrices.
method Cross-View Kernel Transfer (CVKT) with kernel alignment.
result Predicts missing values in kernel matrices using other views' data.
In an era when big data are becoming the norm, there is less concern with the quantity but more with the quality and completeness of the data. In many disciplines, data are collected from heterogeneous sources, resulting in multi-view or multi-modal datasets. The missing data problem has been challenging to address in …
New method for imputing missing data in multi-view data.
problem Large missing data sets in multi-view data.
method Stacked penalized logistic regression (StaPLR) in a dimension-reduced space.
result New method performs competitively with lower computational cost.
The paper explores how missing data problems are related to causal inference.
problem Missing data in experiments makes causal inference difficult.
method The paper reinterprets missing data as a form of causal inference by considering counterfactual variables.
result Identification assumptions in missing data can be encoded using graphical models of counterfactual and observed variables.
A new method for clustering multi-view data with missing views.
problem Clustering multi-view datasets with missing views is challenging.
method OPIMC framework using regularized and weighted matrix factorization.
result OPIMC efficiently clusters multi-view datasets with missing views.
In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, (2) does not require any of the kernels to be complete a priori, and (3) can tackle non-linear kernels. These aspects are necessary in practical app…
Merlin improves robustness of MTSF models to missing data.
problem Suboptimal forecasting performance due to unfixed missing rates in MTSF models.
method Offline knowledge distillation and multi-view contrastive learning.
result Merlin enhances robustness of MTSF models while preserving accuracy.
Proposes a multi-view VAE for imputing missing data from correlated sources.
problem Imputing missing data from multi-view sources with latent space correlation.
method Enforces a joint prior with latent space correlation between VAEs trained on each view.
result More strongly correlated latent spaces are uncovered, enabling effective imputation.
Framework for joint learning of tasks on dementia data with missing values.
problem Lack of multi-task learning, handling time-dependent data, and missing values in dementia forecasting.
method Proposes SSHIBA model using Bayesian variational inference for imputation and combined information from different views.
result SSHIBA model outperforms baselines in predicting diagnosis, ventricle volume, and clinical scores in dementia.
This paper tackles incomplete multi-view clustering with spectral perturbation theory.
problem Realistic clustering scenario where data instances are missing in certain views.
method Spectral perturbation theory and matrix completion method for incomplete similarity matrix.
result The minimization of perturbation risk bounds maximizes the final fusion result across all views.
A new algorithm tackles incomplete multi-view clustering.
problem Incomplete multi-view clustering where views have missing instances.
method DAIMC algorithm using weighted semi-NMF and L2,1-Norm regularized regression. result DAIMC effectively handles missing instances and improves clustering accuracy.
Responding to discussions on missing data models.
problem Clarifying causal and counterfactual views in missing data models.
method Summarizing and responding to comments from discussants.
result Summarized main contributions and addressed each discussion.
HSACC improves multi-view clustering of incomplete data.
problem Challenges in clustering incomplete multi-view data.
method Hierarchical Semantic Alignment and Cooperative Completion framework.
result HSACC outperforms state-of-the-art methods on benchmark datasets.
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…
Missing data enhances privacy in differential privacy.
problem Privacy preservation in datasets with missing values.
method Formalized missing data as a privacy amplification mechanism within differential privacy.
result Incomplete data can yield privacy amplification for differentially private algorithms.
New adaptive models improve prediction accuracy with missing data.
problem Improving prediction accuracy with missing data entries.
method Adaptive optimization approach, learning imputation and regression simultaneously.
result 2-10% improvement in out-of-sample accuracy in strongly non-random missing data settings.
A very simple interpretation of matrix completion problem is introduced based on statistical models. Combined with the well-known results from missing data analysis, such interpretation indicates that matrix completion is still a valid and principled estimation procedure even without the missing completely at random (M…
VAEs struggle with missing data imputation, especially for extreme values.
problem Imputation of missing data in complex, non-linear relationships.
method Investigated variational autoencoders (VAEs) for multiple imputation and improved with β-VAEs.
result β-VAEs provide better uncertainty calibration and avoid false discoveries.
R package `mvs` handles multi-view data for better model performance.
problem Classical machine learning ignores multi-view data structure, limiting interpretability and performance.
method Multi-view stacking (MVS) framework, training on each view separately, cross-validation for weights, ensemble learning.
result MVS improves model performance on multi-view data.
This paper addresses clustering with missing data using Rubin's rules.
problem How to pool partitions and assess instability when data are incomplete after multiple imputation.
method Consensus clustering and bootstrap theory are used to address the problem.
result New rules for pooling partitions and assessing instability are proposed and validated.
PREMA recovers detailed data from aggregated views.
problem Reconstructing detailed data from aggregated views.
method Low-rank tensor factorization.
result Recovery guarantees under certain conditions.
GPCCA integrates multi-modal data with missing values, improving clustering accuracy.
problem Integrating and analyzing multi-modal data with missing values and partial observations.
method Generalized Probabilistic Canonical Correlation Analysis (GPCCA) for unsupervised multi-modal data integration and dimensionality reduction.
result GPCCA outperforms existing methods in capturing essential patterns across modalities and provides robust low-dimensional embeddings.
We propose a nonparametric Bayesian probabilistic latent variable model for multi-view anomaly detection, which is the task of finding instances that have inconsistent views. With the proposed model, all views of a non-anomalous instance are assumed to be generated from a single latent vector. On the other hand, an ano…
This paper tackles time series imputation by identifying and modeling different missing mechanisms.
problem Different types of missing mechanisms (MAR, MNAR) in time series data.
method Proposes a framework for time series imputation by analyzing data generation processes and modeling latent variables via variational inference and normalizing flow.
result Establishes identifiability results for latent variables under nonlinear independent component analysis, showing that latent variables are identifiable.
PbP strategy improves logistic model prediction with missing values.
problem Predicting with missing inputs in logistic models.
method Pattern-by-Pattern (PbP) strategy for logistic models with missing values.
result PbP accurately approximates Bayes probabilities under GPMM across various missing data scenarios.
GRAPE uses graph representation to handle missing data in feature imputation and label prediction.
problem Handling missing data in machine learning tasks.
method GRAPE uses a bipartite graph where observations and features are nodes, and observed feature values are edges. It formulates feature imputation as edge-level prediction and label prediction as node-level prediction, solving these with Graph Neural Networks.
result GRAPE achieves 20% lower mean absolute error for imputation and 10% lower for label prediction compared to state-of-the-art methods.
Proposes CRG_IMSC for better clustering of multi-view data.
problem Lack of effective connectivity in clustering results.
method Directly obtains clustering result with nonnegative constraint; constructs connectivity matrix based on spectral clustering result; uses multiplicative update algorithm.
result Improves clustering performance on benchmark datasets.
Unified framework for multi-view autoencoders simplifies model implementation and evaluation.
problem Inconsistent notation and implementation of multi-view autoencoders.
method Unified mathematical framework, documentation, and Python library.
result Comparable or superior performance in benchmarking experiments.
PLS-SVD struggles with missing data in multimodal datasets, showing a phase transition in performance.
problem Missing data in PLS-SVD for multimodal datasets.
method Replica-symmetric analysis of spiked rectangular random matrices with missing entries.
result PLS-SVD performance transitions from uninformative to informative singular vectors at a critical signal-to-noise threshold.
New method estimates treatment effects from treated and unlabeled units.
problem Estimating ATEs with missing data and weak supervision.
method Develops semiparametric efficient estimators for ATE in PU learning.
result Constructs estimators that achieve semiparametric efficiency bounds.
NAM learns multiview representations with attention for better task performance.
problem Finding optimal combination of multiviews for specific tasks.
method Introduces Neural Attentive Multiview machine with attention mechanism.
result NAM outperforms single view and alternative multiview methods in item recommendations.
A new Thompson Sampling framework handles uncertainty by imputing missing data.
problem Handling uncertainty in contextual bandit problems.
method Generative model to impute missing outcomes, fit policy, and select actions.
result Established a state-of-the-art regret bound that depends on generative model quality.
This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that exten…
Many problems in machine learning and related application areas are fundamentally variants of conditional modeling and sampling across multi-aspect data, either multi-view, multi-modal, or simply multi-group. For example, sampling from the distribution of English sentences conditioned on a given French sentence or samp…
This paper tackles unpaired data in multi-view learning, proposing a new framework and models.
problem Handling unpaired data in multi-view learning, which is more common than paired data.
method Generalized uncorrelated multi-view subspace learning framework with successive alternating approximation (SAA) method.
result Proposed models perform competitively or better than baselines in multi-view feature extraction and multi-modality classification.
Improved sparse-view CT images with deep learning sinogram interpolation.
problem Sparse-view CT images quality improvement with limited projection data.
method Combination of U-Net and residual learning for sinogram interpolation.
result Significantly improved CT image quality (RMSE and SSIM metrics) over standard methods.
Bayesian approach for handling incomplete clinical data.
problem Challenges in machine learning with multimodal, incomplete clinical data.
method Generative and discriminative learning, semi-supervised strategy, imputation of missing views.
result Automatic imputation of missing views and robust inference across different data sources.
Model predicts unseen climate extremes to inform risk planning.
problem Missing unseen climate extremes in historical records.
method DeepX-GAN model capturing spatial dependence.
result Unseen heat extremes disproportionately threaten vulnerable regions.
This paper considers the problem of completing a matrix with many missing entries under the assumption that the columns of the matrix belong to a union of multiple low-rank subspaces. This generalizes the standard low-rank matrix completion problem to situations in which the matrix rank can be quite high or even full r…
We propose JECL, a method for clustering image-caption pairs by training parallel encoders with regularized clustering and alignment objectives, simultaneously learning both representations and cluster assignments. These image-caption pairs arise frequently in high-value applications where structured training data is e…
MTSCI uses diffusion models to impute multivariate time series data with consistency.
problem Imputation of missing values in multivariate time series data.
method MTSCI employs a contrastive complementary mask and mixup mechanism to ensure intra-consistency and inter-consistency.
result MTSCI achieves state-of-the-art performance on multivariate time series imputation tasks.
We present a novel view that unifies two frameworks that aim to solve sequential prediction problems: learning to search (L2S) and recurrent neural networks (RNN). We point out equivalences between elements of the two frameworks. By complementing what is missing from one framework comparing to the other, we introduce a…
A new method for multi-label image classification using multiple feature views.
problem Limited by single-view feature, traditional matrix completion struggles with multi-label image classification.
method Multi-View Matrix Completion (MVMC) framework, combining weighted MC outputs from different views, using cross-validation for weights.
result MVMC framework improves multi-label image classification by exploiting complementary properties of different features and consistent labels.
LLMs generate answers under incomplete context, and their uncertainty should scale with missing information.
problem Evaluating the quality of LLM answers under incomplete context.
method A controlled framework with varying context availability, and two uncertainty measures (sampling-based confidence and response entropy) evaluated on SQuAD.
result Response entropy increases with context removal and explains more variance in accuracy than confidence, suggesting it is a more responsive uncertainty measure.
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…
Develops GNNs for incomplete graphs, improving learning from missing node attributes.
problem Learning from incomplete graphs with missing node attributes.
method Introduces PaGNNs with novel partial aggregation functions for incomplete graph data.
result Demonstrates effectiveness and efficiency of PaGNNs on various datasets.