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JMLR
2010

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Locally Linear Denoising on Image Manifolds

1 years 4 days ago

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We study the problem of image denoising where images are assumed to be samples from low dimensional (sub)manifolds. We propose the algorithm of locally linear denoising. The algor...

Dian Gong, Fei Sha, Gérard G. Medioni

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Id	Views	Title	Status
1	102	Locally Linear Denoising on Image Manifolds	claim
2	94	On Learning with Integral Operators	claim
3	78	Modeling annotator expertise: Learning when everybody knows a bit of something	claim
4	73	Online Learning for Matrix Factorization and Sparse Coding	claim
5	73	Simple Exponential Family PCA	claim
6	71	Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models	claim
7	67	Learning Dissimilarities for Categorical Symbols	claim
8	64	PyBrain	claim
9	62	Multitask Learning for Brain-Computer Interfaces	claim
10	61	Adaptive Step-size Policy Gradients with Average Reward Metric	claim
11	61	Multiple Kernel Learning on the Limit Order Book	claim
12	59	Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization	claim
13	59	Hartigan's Method: k-means Clustering without Voronoi	claim
14	58	A Statistical Implicative Analysis Based Algorithm and MMPC Algorithm for Detecting Multiple Dependencies	claim
15	58	Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering	claim
16	57	Learning with Blocks: Composite Likelihood and Contrastive Divergence	claim
17	56	Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting	claim
18	56	Consensus-Based Distributed Support Vector Machines	claim
19	56	The Feature Selection Path in Kernel Methods	claim
20	54	Feature Selection: An Ever Evolving Frontier in Data Mining	claim
21	54	Combining Experiments to Discover Linear Cyclic Models with Latent Variables	claim
22	54	Matrix-Variate Dirichlet Process Mixture Models	claim
23	53	Using Contextual Representations to Efficiently Learn Context-Free Languages	claim
24	53	On the Foundations of Noise-free Selective Classification	claim
25	53	Sequential Monte Carlo Samplers for Dirichlet Process Mixtures	claim
26	53	Fluid Dynamics Models for Low Rank Discriminant Analysis	claim
27	52	Feature Selection for Text Classification Based on Gini Coefficient of Inequality	claim
28	52	Kronecker Graphs: An Approach to Modeling Networks	claim
29	52	Neural conditional random fields	claim
30	52	Half Transductive Ranking	claim
31	51	HMMPayl: an application of HMM to the analysis of the HTTP Payload	claim
32	51	Reducing Label Complexity by Learning From Bags	claim
33	50	Hierarchical Cost-Sensitive Algorithms for Genome-Wide Gene Function Prediction	claim
34	50	Hierarchical Convex NMF for Clustering Massive Data	claim
35	50	Decision Tree for Dynamic and Uncertain Data Streams	claim
36	49	Learning From Crowds	claim
37	49	Training and Testing Low-degree Polynomial Data Mappings via Linear SVM	claim
38	49	Bayesian variable order Markov models
39	48	On the Rate of Convergence of the Bagged Nearest Neighbor Estimate	claim
40	48	SFO: A Toolbox for Submodular Function Optimization	claim
41	48	Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images	claim
42	48	Efficient Learning of Deep Boltzmann Machines	claim
43	48	Parallelizable Sampling of Markov Random Fields	claim
44	47	Detection of Server-side Web Attacks	claim
45	47	Online Anomaly Detection under Adversarial Impact	claim
46	47	Active Sequential Learning with Tactile Feedback	claim
47	46	Dense Message Passing for Sparse Principal Component Analysis	claim
48	46	Bayesian Algorithms for Causal Data Mining	claim
49	46	Handwritten Text Recognition for Ancient Documents	claim
50	46	Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data	claim
51	46	Approximation of hidden Markov models by mixtures of experts with application to particle filtering	claim
52	45	Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching	claim
53	45	A comparison of AUC estimators in small-sample studies	claim
54	45	Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables	claim
55	45	Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models	claim
56	45	Ellipsoidal Support Vector Machines	claim
57	45	Learning Polyhedral Classifiers Using Logistic Function	claim
58	45	Approximate Tree Kernels	claim
59	45	Conceptual Imitation Learning: An Application to Human-robot Interaction	claim
60	45	Quadratic Programming Feature Selection	claim
61	45	Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers	claim
62	45	Posterior Regularization for Structured Latent Variable Models	claim
63	45	Conditional Density Estimation via Least-Squares Density Ratio Estimation	claim
64	45	On the Impact of Kernel Approximation on Learning Accuracy	claim
65	45	Parametric Herding	claim
66	45	Focused Belief Propagation for Query-Specific Inference	claim
67	44	Evaluation Method for Feature Rankings and their Aggregations for Biomarker Discovery	claim
68	44	Learning to Rank for Personalized News Article Retrieval	claim
69	44	Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization	claim
70	44	Hierarchical Gaussian Process Regression	claim
71	44	A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design	claim
72	44	Topology Selection in Graphical Models of Autoregressive Processes	claim
73	44	A Regularization Approach to Nonlinear Variable Selection	claim
74	44	Collaborative Filtering via Rating Concentration	claim
75	43	Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation	claim
76	43	Mean Field Variational Approximation for Continuous-Time Bayesian Networks	claim
77	43	MOA: Massive Online Analysis	claim
78	43	Feature Selection using Multiple Streams	claim
79	43	Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks	claim
80	42	Causality: Objectives and Assessment	claim
81	42	Automating News Content Analysis: An Application to Gender Bias and Readability	claim
82	42	Efficient Collapsed Gibbs Sampling for Latent Dirichlet Allocation	claim
83	42	Second-Order Bilinear Discriminant Analysis	claim
84	42	Generalized Power Method for Sparse Principal Component Analysis	claim
85	42	Causal learning without DAGs	claim
86	42	Gaussian Processes for Machine Learning (GPML) Toolbox	claim
87	42	An Exponential Model for Infinite Rankings	claim
88	42	Learning the Structure of Deep Sparse Graphical Models	claim
89	42	Approximate parameter inference in a stochastic reaction-diffusion model	claim
90	42	Nonparametric Tree Graphical Models	claim
91	41	Predicting the functions of proteins in Protein-Protein Interaction networks from global information	claim
92	41	Attribute Selection Based on FRiS-Compactness	claim
93	41	On Finding Predictors for Arbitrary Families of Processes	claim
94	41	Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking	claim
95	41	Error-Correcting Ouput Codes Library	claim
96	41	Sparse Semi-supervised Learning Using Conjugate Functions	claim
97	41	Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence	claim
98	41	Practical Approaches to Principal Component Analysis in the Presence of Missing Values	claim
99	41	Multiclass-Multilabel Classification with More Classes than Examples	claim
100	41	Infinite Predictor Subspace Models for Multitask Learning	claim
101	41	Noise-contrastive estimation: A new estimation principle for unnormalized statistical models	claim
102	41	Learning Bayesian Network Structure using LP Relaxations	claim
103	40	Accuracy-Rejection Curves (ARCs) for Comparing Classification Methods with a Reject Option	claim
104	40	MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering	claim
105	40	Pinview: Implicit Feedback in Content-Based Image Retrieval	claim
106	40	Assessment of Cow's Body Condition Score Through Statistical Shape Analysis and Regression Machines	claim
107	40	Maximum Relative Margin and Data-Dependent Regularization	claim
108	40	Sparse Spectrum Gaussian Process Regression	claim
109	40	Importance Sampling for Continuous Time Bayesian Networks	claim
110	40	Tree Decomposition for Large-Scale SVM Problems	claim
111	40	Regularized Discriminant Analysis, Ridge Regression and Beyond	claim
112	40	Variational methods for Reinforcement Learning	claim
113	40	Bayesian structure discovery in Bayesian networks with less space	claim
114	39	Dirichlet Process Mixtures of Generalized Linear Models	claim
115	39	Interactive Pattern Recognition and Human Language Technology for Digital Audiovisual Content Processing	claim
116	39	Cross-associating unlabelled timbre distributions to create expressive musical mappings	claim
117	39	Consistent Nonparametric Tests of Independence	claim
118	39	Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines	claim
119	39	Exclusive Lasso for Multi-task Feature Selection	claim
120	39	Reduced-Rank Hidden Markov Models	claim
121	39	Why are DBNs sparse?	claim
122	39	Graphical Gaussian modelling of multivariate time series with latent variables	claim
123	39	Elliptical slice sampling	claim
124	39	Impossibility Theorems for Domain Adaptation	claim
125	38	The Group Dantzig Selector	claim
126	38	Bundle Methods for Regularized Risk Minimization	claim
127	38	On Spectral Learning	claim
128	38	Incremental Sigmoid Belief Networks for Grammar Learning	claim
129	38	PAC-Bayesian Analysis of Co-clustering and Beyond	claim
130	38	Classification with Incomplete Data Using Dirichlet Process Priors	claim
131	38	Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation	claim
132	38	Rademacher Complexities and Bounding the Excess Risk in Active Learning	claim
133	38	Nonparametric Bayesian Matrix Factorization by Power-EP	claim
134	38	Nonparametric prior for adaptive sparsity	claim
135	38	Semi-Supervised Learning with Max-Margin Graph Cuts	claim
136	38	Exploiting Feature Covariance in High-Dimensional Online Learning	claim
137	37	HOP-MAP: Efficient Message Passing with High Order Potentials	claim
138	37	Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control	claim
139	37	Causal Inference	claim
140	37	Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions	claim
141	37	O-IPCAC and its Application to EEG Classification	claim
142	37	Exploiting the High Predictive Power of Multi-class Subgroups	claim
143	37	On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation	claim
144	37	High Dimensional Inverse Covariance Matrix Estimation via Linear Programming	claim
145	37	Restricted Eigenvalue Properties for Correlated Gaussian Designs	claim
146	37	Inducing Tree-Substitution Grammars	claim
147	37	Learning Translation Invariant Kernels for Classification	claim
148	37	Expectation Truncation and the Benefits of Preselection In Training Generative Models	claim
149	37	Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds	claim
150	37	A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping	claim
151	37	Coherent Inference on Optimal Play in Game Trees	claim
152	36	Discover Local Causal Network around a Target to a Given Depth	claim
153	36	Evaluation of a Bayesian model-based approach in GA studies	claim
154	36	Feature Selection, Association Rules Network and Theory Building	claim
155	36	Increasing Feature Selection Accuracy for L1 Regularized Linear Models	claim
156	36	A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning	claim
157	36	Continuous Time Bayesian Network Reasoning and Learning Engine	claim
158	36	Efficient Multioutput Gaussian Processes through Variational Inducing Kernels	claim
159	36	Mass Fatality Incident Identification based on nuclear DNA evidence	claim
160	36	Bayesian Online Learning for Multi-label and Multi-variate Performance Measures	claim
161	36	Convexity of Proper Composite Binary Losses	claim
162	36	Inductive Principles for Restricted Boltzmann Machine Learning	claim
163	35	Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials	claim
164	35	Beware of the DAG!	claim
165	35	Fast Committee-Based Structure Learning	claim
166	35	Evaluation of Signaling Cascades Based on the Weights from Microarray and ChIP-seq Data	claim
167	35	µTOSS - Multiple hypothesis testing in an open software system	claim
168	35	A Streaming Parallel Decision Tree Algorithm	claim
169	35	A Rotation Test to Verify Latent Structure	claim
170	35	Image Denoising with Kernels Based on Natural Image Relations	claim
171	35	Erratum: SGDQN is Less Careful than Expected	claim
172	35	A Generalized Path Integral Control Approach to Reinforcement Learning	claim
173	35	Learning Non-Stationary Dynamic Bayesian Networks	claim
174	35	Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach	claim
175	35	Empirical Bernstein Boosting	claim
176	35	Factorized Orthogonal Latent Spaces	claim
177	34	Exploiting Within-Clique Factorizations in Junction-Tree Algorithms	claim
178	34	Comparison of Granger Causality and Phase Slope Index	claim
179	34	Feature Extraction for Outlier Detection in High-Dimensional Spaces	claim
180	34	Visualization of Online Discussion Forums	claim
181	34	Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data	claim
182	34	WEKA - Experiences with a Java Open-Source Project	claim
183	34	Stochastic Composite Likelihood	claim
184	34	Spectral Regularization Algorithms for Learning Large Incomplete Matrices	claim
185	34	Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials	claim
186	34	Structured Sparse Principal Component Analysis	claim
187	34	Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation	claim
188	34	On Combining Graph-based Variance Reduction schemes	claim
189	34	Regret Bounds for Gaussian Process Bandit Problems	claim
190	34	Guarantees for Approximate Incremental SVMs	claim
191	33	Unsupervised Aggregation for Classification Problems with Large Numbers of Categories	claim
192	33	When causality matters for prediction	claim
193	33	Reverse Engineering of Asynchronous Boolean Networks	claim
194	33	Variational Relevance Vector Machine for Tabular Data	claim
195	33	Model Selection: Beyond the Bayesian/Frequentist Divide	claim
196	33	Mining Recurring Concept Drifts with Limited Labeled Streaming Data	claim
197	33	Classification Using Geometric Level Sets	claim
198	33	Classification Methods with Reject Option Based on Convex Risk Minimization	claim
199	33	Fast and Scalable Local Kernel Machines	claim
200	33	Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing	claim
201	33	How to Explain Individual Classification Decisions	claim
202	33	Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity	claim
203	33	A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation	claim
204	33	Sufficient covariates and linear propensity analysis	claim
205	33	Nonlinear functional regression: a functional RKHS approach	claim
206	32	Feature Extraction for Machine Learning: Logic-Probabilistic Approach	claim
207	32	Modeling Knowledge Worker Activity	claim
208	32	An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models	claim
209	32	FastInf: An Efficient Approximate Inference Library	claim
210	32	Matrix Completion from Noisy Entries	claim
211	32	Learnability, Stability and Uniform Convergence	claim
212	32	Evolving Static Representations for Task Transfer	claim
213	32	Characterization, Stability and Convergence of Hierarchical Clustering Methods	claim
214	32	Negative Results for Active Learning with Convex Losses	claim
215	32	State-Space Inference and Learning with Gaussian Processes	claim
216	32	Gaussian processes with monotonicity information	claim
217	32	On the Convergence Properties of Contrastive Divergence	claim
218	32	Collaborative Filtering on a Budget	claim
219	32	Bayesian Gaussian Process Latent Variable Model	claim
220	32	Online Passive-Aggressive Algorithms on a Budget	claim
221	32	Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity	claim
222	31	Model-Free Monte Carlo-like Policy Evaluation	claim
223	31	TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries	claim
224	31	Finite-sample Analysis of Bellman Residual Minimization	claim
225	31	Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure	claim
226	31	Covariance in Unsupervised Learning of Probabilistic Grammars	claim
227	31	The SHOGUN Machine Learning Toolbox	claim
228	31	Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes	claim
229	31	Regret Bounds and Minimax Policies under Partial Monitoring	claim
230	31	Hilbert Space Embeddings and Metrics on Probability Measures	claim
231	31	Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases	claim
232	31	Boosted Optimization for Network Classification	claim
233	31	Structured Prediction Cascades	claim
234	31	Contextual Multi-Armed Bandits	claim
235	31	On the relation between universality, characteristic kernels and RKHS embedding of measures	claim
236	31	Optimal Allocation Strategies for the Dark Pool Problem	claim
237	30	Event based text mining for integrated network construction	claim
238	30	Effective Wrapper-Filter hybridization through GRASP Schemata	claim
239	30	Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net	claim
240	30	Semi-Supervised Learning via Generalized Maximum Entropy	claim
241	30	Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries	claim
242	30	Learning Nonlinear Dynamic Models from Non-sequenced Data	claim
243	30	Discriminative Topic Segmentation of Text and Speech	claim
244	30	Efficient Reductions for Imitation Learning	claim
245	29	A Convergent Online Single Time Scale Actor Critic Algorithm	claim
246	29	Learning Instance-Specific Predictive Models	claim
247	29	Identifying Cause and Effect on Discrete Data using Additive Noise Models	claim
248	29	A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra	claim
249	29	Supervised Dimension Reduction Using Bayesian Mixture Modeling	claim
250	29	Improving posterior marginal approximations in latent Gaussian models	claim
251	28	Causal Discovery as a Game	claim
252	28	Descent Methods for Tuning Parameter Refinement	claim
253	28	Learning Causal Structure from Overlapping Variable Sets	claim
254	28	Understanding the difficulty of training deep feedforward neural networks	claim
255	28	Bayesian Generalized Kernel Models	claim
256	27	Distinguishing between cause and effect	claim
257	27	Gap Between Theory and Practice: Noise Sensitive Word Alignment in Machine Translation	claim
258	27	An Efficient Explanation of Individual Classifications using Game Theory	claim
259	27	Why Does Unsupervised Pre-training Help Deep Learning?	claim
260	26	The Coding Divergence for Measuring the Complexity of Separating Two Sets	claim
261	26	Efficient Algorithms for Conditional Independence Inference	claim
262	26	Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms	claim
263	26	Kernel Partial Least Squares is Universally Consistent	claim
264	25	Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes	claim
265	25	Single versus Multiple Sorting in All Pairs Similarity Search	claim
266	24	Content-based Image Retrieval with Multinomial Relevance Feedback	claim
267	23	On utility of gene set signatures in gene expression-based cancer class prediction	claim
268	23	Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels	claim
269	23	An Alternative Prior Process for Nonparametric Bayesian Clustering	claim
270	23	Learning Policy Improvements with Path Integrals	claim
271	22	Posterior distributions are computable from predictive distributions	claim
272	21	Introduction to Causal Inference	claim
273	21	Composite Binary Losses	claim
274	20	Semi-Supervised Novelty Detection	claim
275	20	Inference and Learning in Networks of Queues	claim
276	18	Model-based Boosting 2.0	claim
277	17	A Potential-based Framework for Online Multi-class Learning with Partial Feedback	claim

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