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JMLR
2010

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Locally Linear Denoising on Image Manifolds

13 years 6 months 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	367	Locally Linear Denoising on Image Manifolds	claim
2	230	Learning Dissimilarities for Categorical Symbols	claim
3	227	PyBrain	claim
4	225	Hartigan's Method: k-means Clustering without Voronoi	claim
5	218	Simple Exponential Family PCA	claim
6	211	Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering	claim
7	206	Learning Translation Invariant Kernels for Classification	claim
8	202	Learning the Structure of Deep Sparse Graphical Models	claim
9	198	On Learning with Integral Operators	claim
10	198	Online Anomaly Detection under Adversarial Impact	claim
11	195	Online Learning for Matrix Factorization and Sparse Coding	claim
12	194	Graphical Gaussian modelling of multivariate time series with latent variables	claim
13	194	A Statistical Implicative Analysis Based Algorithm and MMPC Algorithm for Detecting Multiple Dependencies	claim
14	192	Inducing Tree-Substitution Grammars	claim
15	192	Efficient Learning of Deep Boltzmann Machines	claim
16	191	Noise-contrastive estimation: A new estimation principle for unnormalized statistical models	claim
17	189	Adaptive Step-size Policy Gradients with Average Reward Metric	claim
18	187	SFO: A Toolbox for Submodular Function Optimization	claim
19	186	Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting	claim
20	185	Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers	claim
21	185	HMMPayl: an application of HMM to the analysis of the HTTP Payload	claim
22	185	Multiple Kernel Learning on the Limit Order Book	claim
23	184	Sequential Monte Carlo Samplers for Dirichlet Process Mixtures	claim
24	182	Quadratic Programming Feature Selection	claim
25	182	Decision Tree for Dynamic and Uncertain Data Streams	claim
26	179	PAC-Bayesian Analysis of Co-clustering and Beyond	claim
27	177	Multitask Learning for Brain-Computer Interfaces	claim
28	175	Bayesian variable order Markov models
29	175	Hierarchical Convex NMF for Clustering Massive Data	claim
30	173	Conditional Density Estimation via Least-Squares Density Ratio Estimation	claim
31	173	Elliptical slice sampling	claim
32	173	Collaborative Filtering via Rating Concentration	claim
33	172	Modeling annotator expertise: Learning when everybody knows a bit of something	claim
34	170	A Streaming Parallel Decision Tree Algorithm	claim
35	169	Consensus-Based Distributed Support Vector Machines	claim
36	169	Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images	claim
37	169	Matrix-Variate Dirichlet Process Mixture Models	claim
38	169	Focused Belief Propagation for Query-Specific Inference	claim
39	166	Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models	claim
40	165	Learning with Blocks: Composite Likelihood and Contrastive Divergence	claim
41	165	Causal Inference	claim
42	165	Feature Selection: An Ever Evolving Frontier in Data Mining	claim
43	164	Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms	claim
44	163	Active Sequential Learning with Tactile Feedback	claim
45	163	Dense Message Passing for Sparse Principal Component Analysis	claim
46	163	Detection of Server-side Web Attacks	claim
47	162	A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design	claim
48	161	Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization	claim
49	161	Training and Testing Low-degree Polynomial Data Mappings via Linear SVM	claim
50	161	Empirical Bernstein Boosting	claim
51	161	Accuracy-Rejection Curves (ARCs) for Comparing Classification Methods with a Reject Option	claim
52	161	Feature Selection for Text Classification Based on Gini Coefficient of Inequality	claim
53	160	Neural conditional random fields	claim
54	159	Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks	claim
55	159	Semi-Supervised Learning with Max-Margin Graph Cuts	claim
56	158	Restricted Eigenvalue Properties for Correlated Gaussian Designs	claim
57	158	Topology Selection in Graphical Models of Autoregressive Processes	claim
58	157	Kronecker Graphs: An Approach to Modeling Networks	claim
59	157	Combining Experiments to Discover Linear Cyclic Models with Latent Variables	claim
60	157	Why are DBNs sparse?	claim
61	157	Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control	claim
62	156	Classification with Incomplete Data Using Dirichlet Process Priors	claim
63	156	Collaborative Filtering on a Budget	claim
64	155	Approximate Tree Kernels	claim
65	155	Structured Sparse Principal Component Analysis	claim
66	155	Bayesian Gaussian Process Latent Variable Model	claim
67	154	MOA: Massive Online Analysis	claim
68	154	Infinite Predictor Subspace Models for Multitask Learning	claim
69	153	Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data	claim
70	153	Feature Extraction for Outlier Detection in High-Dimensional Spaces	claim
71	152	Stochastic Composite Likelihood	claim
72	152	The SHOGUN Machine Learning Toolbox	claim
73	152	Bayesian Generalized Kernel Models	claim
74	151	Understanding the difficulty of training deep feedforward neural networks	claim
75	151	The Feature Selection Path in Kernel Methods	claim
76	150	Approximate parameter inference in a stochastic reaction-diffusion model	claim
77	150	Supervised Dimension Reduction Using Bayesian Mixture Modeling	claim
78	149	Coherent Inference on Optimal Play in Game Trees	claim
79	149	Learning Bayesian Network Structure using LP Relaxations	claim
80	149	Fast Committee-Based Structure Learning	claim
81	149	Assessment of Cow's Body Condition Score Through Statistical Shape Analysis and Regression Machines
82	148	Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation	claim
83	148	A Generalized Path Integral Control Approach to Reinforcement Learning	claim
84	147	Image Denoising with Kernels Based on Natural Image Relations	claim
85	147	Gaussian Processes for Machine Learning (GPML) Toolbox	claim
86	147	Spectral Regularization Algorithms for Learning Large Incomplete Matrices	claim
87	146	Nonparametric Tree Graphical Models	claim
88	146	Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking	claim
89	145	Kernel Partial Least Squares is Universally Consistent	claim
90	145	Parallelizable Sampling of Markov Random Fields	claim
91	145	Event based text mining for integrated network construction	claim
92	144	Practical Approaches to Principal Component Analysis in the Presence of Missing Values	claim
93	144	Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables	claim
94	143	A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning	claim
95	143	Incremental Sigmoid Belief Networks for Grammar Learning	claim
96	143	Regularized Discriminant Analysis, Ridge Regression and Beyond	claim
97	143	Rademacher Complexities and Bounding the Excess Risk in Active Learning	claim
98	143	Beware of the DAG!	claim
99	141	FastInf: An Efficient Approximate Inference Library	claim
100	141	Pinview: Implicit Feedback in Content-Based Image Retrieval	claim
101	141	Hierarchical Gaussian Process Regression	claim
102	140	Mean Field Variational Approximation for Continuous-Time Bayesian Networks	claim
103	140	Learning From Crowds	claim
104	140	Learning Non-Stationary Dynamic Bayesian Networks	claim
105	139	On the Foundations of Noise-free Selective Classification	claim
106	139	Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines	claim
107	139	Causal learning without DAGs	claim
108	138	Handwritten Text Recognition for Ancient Documents	claim
109	137	Importance Sampling for Continuous Time Bayesian Networks	claim
110	137	Covariance in Unsupervised Learning of Probabilistic Grammars	claim
111	137	HOP-MAP: Efficient Message Passing with High Order Potentials	claim
112	136	High Dimensional Inverse Covariance Matrix Estimation via Linear Programming	claim
113	136	Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes	claim
114	136	Reducing Label Complexity by Learning From Bags	claim
115	136	Predicting the functions of proteins in Protein-Protein Interaction networks from global information	claim
116	136	Evaluation Method for Feature Rankings and their Aggregations for Biomarker Discovery	claim
117	136	Conceptual Imitation Learning: An Application to Human-robot Interaction	claim
118	135	Bundle Methods for Regularized Risk Minimization	claim
119	135	An Exponential Model for Infinite Rankings	claim
120	135	Structured Prediction Cascades	claim
121	135	Finite-sample Analysis of Bellman Residual Minimization	claim
122	134	Using Contextual Representations to Efficiently Learn Context-Free Languages	claim
123	134	Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity	claim
124	134	Half Transductive Ranking	claim
125	134	Bayesian Algorithms for Causal Data Mining	claim
126	134	Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions	claim
127	133	Exclusive Lasso for Multi-task Feature Selection	claim
128	133	Hierarchical Cost-Sensitive Algorithms for Genome-Wide Gene Function Prediction	claim
129	132	Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence	claim
130	132	On the Impact of Kernel Approximation on Learning Accuracy	claim
131	130	A Regularization Approach to Nonlinear Variable Selection	claim
132	130	MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering	claim
133	129	On Finding Predictors for Arbitrary Families of Processes	claim
134	129	Expectation Truncation and the Benefits of Preselection In Training Generative Models	claim
135	129	Efficient Multioutput Gaussian Processes through Variational Inducing Kernels	claim
136	129	Approximation of hidden Markov models by mixtures of experts with application to particle filtering	claim
137	129	Learning Polyhedral Classifiers Using Logistic Function	claim
138	128	On the Rate of Convergence of the Bagged Nearest Neighbor Estimate	claim
139	128	Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models	claim
140	128	Learning Causal Structure from Overlapping Variable Sets	claim
141	128	Fluid Dynamics Models for Low Rank Discriminant Analysis	claim
142	128	Optimal Allocation Strategies for the Dark Pool Problem	claim
143	127	Cross-associating unlabelled timbre distributions to create expressive musical mappings	claim
144	127	Content-based Image Retrieval with Multinomial Relevance Feedback	claim
145	126	Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach	claim
146	126	Causality: Objectives and Assessment	claim
147	125	Continuous Time Bayesian Network Reasoning and Learning Engine	claim
148	125	Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation	claim
149	125	Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds	claim
150	125	Variational methods for Reinforcement Learning	claim
151	125	Regret Bounds for Gaussian Process Bandit Problems	claim
152	125	On utility of gene set signatures in gene expression-based cancer class prediction	claim
153	125	Variational Relevance Vector Machine for Tabular Data	claim
154	124	Consistent Nonparametric Tests of Independence	claim
155	124	Multiclass-Multilabel Classification with More Classes than Examples	claim
156	123	Maximum Relative Margin and Data-Dependent Regularization	claim
157	123	Inductive Principles for Restricted Boltzmann Machine Learning	claim
158	123	µTOSS - Multiple hypothesis testing in an open software system	claim
159	121	Sparse Semi-supervised Learning Using Conjugate Functions	claim
160	121	A comparison of AUC estimators in small-sample studies	claim
161	121	Efficient Collapsed Gibbs Sampling for Latent Dirichlet Allocation	claim
162	120	Effective Wrapper-Filter hybridization through GRASP Schemata	claim
163	119	A Convergent Online Single Time Scale Actor Critic Algorithm	claim
164	119	Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data	claim
165	119	Semi-Supervised Learning via Generalized Maximum Entropy	claim
166	119	Online Passive-Aggressive Algorithms on a Budget	claim
167	119	Factorized Orthogonal Latent Spaces	claim
168	119	The Group Dantzig Selector	claim
169	119	The Coding Divergence for Measuring the Complexity of Separating Two Sets	claim
170	118	On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation	claim
171	118	Hilbert Space Embeddings and Metrics on Probability Measures	claim
172	118	Gaussian processes with monotonicity information	claim
173	118	Exploiting Within-Clique Factorizations in Junction-Tree Algorithms	claim
174	118	Dirichlet Process Mixtures of Generalized Linear Models	claim
175	118	Learning to Rank for Personalized News Article Retrieval	claim
176	117	Bayesian Online Learning for Multi-label and Multi-variate Performance Measures	claim
177	117	Comparison of Granger Causality and Phase Slope Index	claim
178	117	Exploiting the High Predictive Power of Multi-class Subgroups	claim
179	116	Feature Selection, Association Rules Network and Theory Building	claim
180	116	Automating News Content Analysis: An Application to Gender Bias and Readability	claim
181	115	Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes	claim
182	115	Generalized Power Method for Sparse Principal Component Analysis	claim
183	115	Fast and Scalable Local Kernel Machines	claim
184	115	Bayesian structure discovery in Bayesian networks with less space	claim
185	115	Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching	claim
186	115	O-IPCAC and its Application to EEG Classification	claim
187	114	Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing	claim
188	114	On the relation between universality, characteristic kernels and RKHS embedding of measures	claim
189	114	Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials	claim
190	113	Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure	claim
191	112	Sparse Spectrum Gaussian Process Regression	claim
192	112	Reduced-Rank Hidden Markov Models	claim
193	111	Single versus Multiple Sorting in All Pairs Similarity Search	claim
194	111	An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models	claim
195	110	Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization	claim
196	110	Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net	claim
197	110	Nonlinear functional regression: a functional RKHS approach	claim
198	110	Attribute Selection Based on FRiS-Compactness	claim
199	109	Matrix Completion from Noisy Entries	claim
200	108	Second-Order Bilinear Discriminant Analysis	claim
201	108	WEKA - Experiences with a Java Open-Source Project	claim
202	108	Tree Decomposition for Large-Scale SVM Problems	claim
203	108	Feature Selection using Multiple Streams	claim
204	108	Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation	claim
205	108	Mining Recurring Concept Drifts with Limited Labeled Streaming Data	claim
206	107	Error-Correcting Ouput Codes Library	claim
207	107	Learning Instance-Specific Predictive Models	claim
208	107	Modeling Knowledge Worker Activity	claim
209	106	Why Does Unsupervised Pre-training Help Deep Learning?	claim
210	106	Improving posterior marginal approximations in latent Gaussian models	claim
211	105	Classification Methods with Reject Option Based on Convex Risk Minimization	claim
212	105	Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials	claim
213	105	A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra	claim
214	105	On the Convergence Properties of Contrastive Divergence	claim
215	105	Causal Discovery as a Game	claim
216	105	When causality matters for prediction	claim
217	105	Evaluation of Signaling Cascades Based on the Weights from Microarray and ChIP-seq Data	claim
218	104	How to Explain Individual Classification Decisions	claim
219	104	Learnability, Stability and Uniform Convergence	claim
220	104	Increasing Feature Selection Accuracy for L1 Regularized Linear Models	claim
221	103	Regret Bounds and Minimax Policies under Partial Monitoring	claim
222	103	Learning Nonlinear Dynamic Models from Non-sequenced Data	claim
223	102	Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels	claim
224	102	Efficient Algorithms for Conditional Independence Inference	claim
225	102	Discover Local Causal Network around a Target to a Given Depth	claim
226	102	Visualization of Online Discussion Forums	claim
227	101	Classification Using Geometric Level Sets	claim
228	101	A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping	claim
229	101	Efficient Reductions for Imitation Learning	claim
230	101	Impossibility Theorems for Domain Adaptation	claim
231	101	Exploiting Feature Covariance in High-Dimensional Online Learning	claim
232	100	Parametric Herding	claim
233	100	Discriminative Topic Segmentation of Text and Speech	claim
234	100	Contextual Multi-Armed Bandits	claim
235	100	Ellipsoidal Support Vector Machines	claim
236	99	An Efficient Explanation of Individual Classifications using Game Theory	claim
237	99	Characterization, Stability and Convergence of Hierarchical Clustering Methods	claim
238	99	On Combining Graph-based Variance Reduction schemes	claim
239	97	Guarantees for Approximate Incremental SVMs	claim
240	97	Evaluation of a Bayesian model-based approach in GA studies	claim
241	96	Posterior Regularization for Structured Latent Variable Models	claim
242	95	Nonparametric Bayesian Matrix Factorization by Power-EP	claim
243	95	Feature Extraction for Machine Learning: Logic-Probabilistic Approach	claim
244	94	A Rotation Test to Verify Latent Structure	claim
245	93	Erratum: SGDQN is Less Careful than Expected	claim
246	93	Sufficient covariates and linear propensity analysis	claim
247	93	Distinguishing between cause and effect	claim
248	93	Interactive Pattern Recognition and Human Language Technology for Digital Audiovisual Content Processing	claim
249	92	Posterior distributions are computable from predictive distributions	claim
250	91	Composite Binary Losses	claim
251	91	Convexity of Proper Composite Binary Losses	claim
252	89	Mass Fatality Incident Identification based on nuclear DNA evidence	claim
253	88	Descent Methods for Tuning Parameter Refinement	claim
254	88	Inference and Learning in Networks of Queues	claim
255	88	Unsupervised Aggregation for Classification Problems with Large Numbers of Categories	claim
256	87	Boosted Optimization for Network Classification	claim
257	86	Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries	claim
258	85	Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases	claim
259	84	Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity	claim
260	83	Evolving Static Representations for Task Transfer	claim
261	82	On Spectral Learning	claim
262	82	Negative Results for Active Learning with Convex Losses	claim
263	82	TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries	claim
264	78	An Alternative Prior Process for Nonparametric Bayesian Clustering	claim
265	77	Gap Between Theory and Practice: Noise Sensitive Word Alignment in Machine Translation	claim
266	72	Model-Free Monte Carlo-like Policy Evaluation	claim
267	72	Reverse Engineering of Asynchronous Boolean Networks	claim
268	70	Identifying Cause and Effect on Discrete Data using Additive Noise Models	claim
269	70	State-Space Inference and Learning with Gaussian Processes	claim
270	69	Nonparametric prior for adaptive sparsity	claim
271	68	A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation	claim
272	66	Model Selection: Beyond the Bayesian/Frequentist Divide	claim
273	62	Introduction to Causal Inference	claim
274	61	Model-based Boosting 2.0	claim
275	61	Learning Policy Improvements with Path Integrals	claim
276	59	A Potential-based Framework for Online Multi-class Learning with Partial Feedback	claim
277	53	Semi-Supervised Novelty Detection	claim

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