Logistic LDA applies LDA to non-categorical data.
problem Applying LDA to non-categorical data is challenging.
method Logistic LDA, a discriminative variant of LDA.
result Logistic LDA can learn from unlabeled data.
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48 results for “LDA model”
Deep generative LDAcs.LG
Reinterprets DNF as a deep generative LDA model for complex data.
problem Limited applicability of LDA in complex data scenarios.
method Proposes a discriminative normalization flow (DNF) model and interprets it as a deep generative LDA.
result DNF and its subspace version outperform conventional LDA in modeling complex data.
A 'Gibbs-Newton' Technique for Enhanced Inference of Multivariate Polya Parameters and Topic Modelscs.LG
Proposes LDA-GN for better inference of LDA hyper-parameters.
problem Improving inference of LDA hyper-parameters.
method 'Gibbs-Newton' technique for learning multivariate Polya distributions.
result 'Gibbs-Newton' outperforms existing methods in LDA-GN.
In latent Dirichlet allocation (LDA), topics are multinomial distributions over the entire vocabulary. However, the vocabulary usually contains many words that are not relevant in forming the topics. We adopt a variable selection method widely used in statistical modeling as a dimension reduction tool and combine it wi…
Simple Deep LDA models achieve accuracy competitive with softmax baselines.
problem Training Deep LDA models by maximum likelihood estimation leads to overlapping or collapsed class clusters.
method Proposed a constrained Deep LDA formulation with geometric constraints to fix class means and covariance.
result MLE becomes stable under geometric constraints, yielding well-separated class clusters.
E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Timecs.LG
E-LDA offers faster, interpretable LDA topic models.
problem Inferring topics in LDA topic models with strong guarantees.
method Non-gradient combinatorial approach for faster convergence.
result Logarithmic parallel computation time and interpretability.
Paper tackles privacy issues in LDA model training.
problem Privacy leakage in LDA model training.
method Developed privacy monitoring and locally private LDA training algorithms.
result Demonstrated effectiveness of locally private LDA training on crowdsourced data.
A new streaming Gibbs sampling method improves online LDA model perplexity.
problem Online learning of LDA models with poor performance.
method Streaming Gibbs Sampling (SGS) as an online extension of collapsed Gibbs sampling (CGS).
result SGS achieves similar perplexity to CGS, better than SVB.
New method improves reliability of LDA topic modeling by assessing stability across replicated runs.
problem LDA's reproducibility issues due to initial values and Gibbs sampling.
method Cluster replicated LDA runs using modified Jaccard coefficient and pruning algorithm.
result New measure S-CLOP quantifies LDA topic stability, improving reproducibility.
MILDA uses unlabelled data to compute LDA projections.
problem Training LDA models with unlabelled data.
method Minimal prior information to compute LDA projection vector.
result MILDA closely matches supervised LDA performance and adapts to non-stationary data.
Derives VMP for LDA, simplifying inference for topic modeling.
problem Manual derivation of VMP equations for LDA is challenging and time-consuming.
method Detailed derivation of VMP update equations for LDA.
result Enables easier implementation of VMP for LDA models.
Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classificationstat.ML
LDA-XGB1 balances fairness and accuracy in lending models.
problem Fair lending practices and model interpretability in binary classification.
method Biobjective optimization using binning and information value, leveraging XGBoost.
result Achieves effective balance between accuracy, fairness, and interpretability.
The sBIC outperforms other model selection criteria in LDA topic modeling.
problem Selecting the optimal number of topics in Latent Dirichlet Allocation (LDA) models.
method Monte Carlo simulations comparing sBIC to other criteria.
result sBIC is superior for choosing the number of topics in LDA models.
LDTA expands LDA's topic modeling capacity with tree-structured priors.
problem Limited expressiveness of Dirichlet priors in LDA for complex topic relationships.
method Introduces Latent Dirichlet-Tree Allocation (LDTA) with Dirichlet-Tree (DT) priors, and develops universal mean-field variational inference and Expectation Propagation.
result LDTA enables expressive, tree-structured priors over topic proportions, expanding modeling capacity of LDA.
Improved topic modeling captures temporal relationships in speech.
problem Lack of temporal information in LDA for speech analysis.
method Temporal Markov chain extension to LDA for acoustic unit discovery.
result Improved phone segmentation results compared to base LDA.
Proposes a sparse LDA for partially labeled data.
problem Classification with partially labeled data in high-dimensional settings.
method Semi-supervised sparse LDA classifier using difference-convex algorithm.
result Boosts classification performance with unlabeled data.
A new LDA variant improves multi-label classification performance.
problem Improving multi-label classification performance.
method Saliency-based weights redefine between-class and within-class scatter matrices for multi-label classification.
result The proposed method leads to performance improvements in various multi-label classification problems.
A new LDA model with covariates for mixed-membership clusters.
problem Modeling mixed-membership clusters in discrete data with covariates.
method Negative binomial regression embedded within LDA, slice sampling within Gibbs sampling.
result Model successfully retrieves true parameter values and predicts cluster abundances using covariates.
This paper defines less discriminatory algorithms and explores their feasibility.
problem Creating algorithms that are less discriminatory while meeting business needs.
method Formal definition of less discriminatory algorithms, evaluation of feasibility, and search for alternatives.
result Formal definitions of less discriminatory algorithms face challenges due to lack of held-out data, necessitating a reliance on reasonableness standards.
Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDAcs.LG
A fast cross-validation method for high-dimensional data.
problem High computational cost of least-squares models in high-dimensional datasets.
method Analytical approach for k-fold cross-validation without explicit model training.
result Up to 10,000x faster than standard approach in high-dimensional data.
DNLL loss improves deep LDA accuracy and consistency.
problem Pathological solutions in unconstrained Deep LDA.
method Introducing Discriminative Negative Log-Likelihood (DNLL) loss.
result Deep LDA trained with DNLL produces clean latent spaces and better calibrated probabilities.
PM-LDA models partial memberships for image segmentation.
problem Crisp segmentation limitations in topic models for images.
method Developed PM-LDA model and estimation algorithms.
result PM-LDA produces both crisp and soft segmentations.
Explains LDA and QDA for binary and multiple classes.
problem Classification methods in statistical and probabilistic learning.
method Optimization of decision boundaries, estimation of parameters, relation to other methods.
result Equivalence of LDA and Fisher discriminant analysis.
A new method for LDA using randomized Kaczmarz improves accuracy for large datasets.
problem Efficiently performing LDA on large datasets.
method Randomized Kaczmarz method applied to linear discriminant analysis.
result The method achieves comparable accuracy to full data LDA.
Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDAstat.ML
Improved LDA parameter estimation from sparse samples.
problem Efficiently estimating LDA parameters from limited data.
method Adapting soft clustering to CGS for averaging over multiple samples.
result Consistent advantage over traditional methods in parameter estimation.
LDA-GO improves LDA for high-dimensional data via gradient optimization.
problem LDA struggles in high-dimensional settings due to unreliable covariance matrix estimation.
method LDA-GO learns a low-rank precision matrix via gradient optimization, automatically selecting between Gaussian likelihood and cross-entropy loss.
result LDA-GO outperforms other LDA variants in sparse-signal high-dimensional regimes.
A new sampler speeds up LDA topic modeling for big data.
problem Training LDA on large corpora is slow and requires dense memory storage.
method Uses a Pólya-urn-based approximation in a sparse partially collapsed sampler.
result The new sampler is faster and asymptotically exact.
Efficiently estimates sparse LDA across multiple machines.
problem Sparse LDA estimation in high-dimensional data.
method Distributed estimation with debiasing and aggregation.
result Aggregated estimator achieves same statistical rate as centralized method.
Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Lookcs.LG
LDA improves image classification accuracy with fewer features.
problem Fine-grained image classification with pretrained features.
method Supervised dimensionality reduction with LDA before linear probing.
result LDA improves accuracy over full features in 11 out of 12 configurations.
Unification of HDP and LDA Models for Optimal Topic Clustering of Subject Specific Question Bankscs.IR
Unified HDP and LDA models for efficient topic clustering of online course queries.
problem Efficiently cluster and answer subject-specific online course queries.
method Use Hierarchical Dirichlet Process (HDP) to optimize topic number for Latent Dirichlet Allocation (LDA) model runs.
result Achieve optimal clustering efficiency by recursively applying LDA on effective topics.
APS-LDA scales topic modeling to web-scale data using Spark.
problem Scaling topic models to web-scale data.
method Integrates Spark with LDA using an asynchronous parameter server.
result APS-LDA processes up to 135x more data and 10x more topics than Spark LDA.
Paper calculates the exact error of LDA models.
problem Bayesian generalization error in Latent Dirichlet Allocation (LDA).
method Theoretical analysis of learning coefficient using algebraic geometry.
result Exact asymptotic form of LDA's generalization error.
EFDA extends LDA to non-Gaussian models using exponential families.
problem Classifying non-Gaussian data with LDA's limitations.
method EFDA uses exponential families to derive closed-form estimators for natural parameters and a linear decision rule.
result EFDA matches LDA's accuracy while reducing ECE by 2-6x, proving asymptotic calibration and efficiency.
PM-LDA models allow partial topic memberships for image patches.
problem Crisp segmentation limits topic models to one topic per image patch.
method Developed PM-LDA for partial topic memberships and an estimation algorithm.
result PM-LDA can produce both crisp and soft segmentations.
New topic model uses combinatorial optimization for faster, better results.
problem Improving topic modeling efficiency and quality.
method Derived a new objective function from LDA by small-variance asymptotics, solved combinatorially.
result New algorithm outperforms existing probabilistic methods.
LDA identifies latent topics in CFPB consumer complaints over time.
problem Identify latent topics in CFPB consumer complaints for better regulation effectiveness.
method Latent Dirichlet Allocation (LDA) for topic modeling of consumer complaints.
result Time trends of latent topics reveal regulatory effectiveness and consumer protection issues.
We review three algorithms for Latent Dirichlet Allocation (LDA). Two of them are variational inference algorithms: Variational Bayesian inference and Online Variational Bayesian inference and one is Markov Chain Monte Carlo (MCMC) algorithm -- Collapsed Gibbs sampling. We compare their time complexity and performance.…
WarpLDA improves LDA efficiency by 5-15x, learning up to 1M topics from 100M docs.
problem Improving efficiency of LDA for large-scale document analysis.
method Developed WarpLDA, an O(1) time complexity LDA sampler with reduced cache misses.
result 5-15x faster than state-of-the-art Metropolis-Hastings based LightLDA.
Paper proposes a new robust LDA method using L1,2-norm ratio minimization.
problem Outliers sensitivity in traditional LDA methods.
method L1,2-norm ratio minimization, novel efficient algorithm.
result The proposed method is effective and converges fast.
AVITM improves topic modeling inference speed and interpretability.
problem Difficulties in applying AEVB to topic models, especially with Dirichlet priors and component collapsing.
method Autoencoding Variational Inference for Topic Models (AVITM) using an inference network.
result AVITM matches traditional methods in accuracy with significantly better inference time and without variational optimization on test data.
Incremental variational inference speeds up LDA processing.
problem Efficiently processing large document collections in LDA.
method Inspired by incremental EM, introduces incremental variational inference for LDA.
result Incremental LDA converges faster and monotonically improves variational bound.
SWRLDA improves LDA for multi-class classification with edge classes.
problem LDA's vulnerability to edge classes causing biased mean and large distances.
method Self-weighted robust LDA with l21-norm distance criterion.
result SWRLDA outperforms other methods on synthetic and real-world datasets.
ALBU improves LDA performance on small datasets.
problem Limited data impairs variational Bayes in LDA.
method Approximate Loopy Belief message passing algorithm.
result ALBU learns latent distributions more accurately than VB, especially for smaller data sets.
This paper trains topic models on neuromorphic systems for low energy.
problem Training topic models on general purpose computers is energy-consuming.
method Developed SNNs for LDA and pLSI training on neuromorphic systems.
result SNNs can train topic models with comparable performance to GPCs but lower energy.
New SVI algorithms improve inference in sparse latent factor models.
problem Improving inference in sparse latent factor models using SVI.
method Developed new SVI algorithms for BPFA, tested on various datasets.
result SVI with Gibbs sampling maintains posterior dependencies effectively in BPFA.
SaberLDA learns a large number of topics from big text data on GPUs.
problem Handling large number of topics with existing GPU-based LDA systems.
method Sparsity-aware algorithm, novel data layout, warp-based sampling kernel, sparse count matrix updating.
result SaberLDA learns up to 10,000 topics from billions of tokens in a few hours.
Originally designed to model text, topic modeling has become a powerful tool for uncovering latent structure in domains including medicine, finance, and vision. The goals for the model vary depending on the application: in some cases, the discovered topics may be used for prediction or some other downstream task. In ot…
Paper compares NMF and LDA for topic labeling in customer communications.
problem Automatically labeling topics in customer inquiries.
method Uses NMF and LDA for topic mining and labeling.
result Proposes methods for automated topic subject labeling.