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Submodular feature selection for highdimensional acoustic score spaces
 In ICASSP
, 2013
"... We apply methods for selecting subsets of dimensions from highdimensional score spaces, and subsets of data for training, using submodular function optimization. Submodular functions provide theoretical performance guarantees while simultaneously retaining extremely fast and scalable optimizatio ..."
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Cited by 7 (5 self)
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We apply methods for selecting subsets of dimensions from highdimensional score spaces, and subsets of data for training, using submodular function optimization. Submodular functions provide theoretical performance guarantees while simultaneously retaining extremely fast and scalable optimization via an accelerated greedy algorithm. We evaluate this approach on two applications: data subset selection for phone recognizer training, and semisupervised learning for phone segment classification. Interestingly, the first application uses submodularity twice: first for score space subselection and then for data subset selection. Our approach is computationally efficient but still consistently outperforms a number of baseline methods. Index Terms — feature selection, Fisher kernel, acoustic similarity, graphbased learning, submodularity
Fast Multiclass Segmentation using Diffuse Interface Methods on Graphs
"... We present two graphbased algorithms for multiclass segmentation of highdimensional data. The algorithms use a diffuse interface model based on the GinzburgLandau functional, related to total variation compressed sensing and image processing. A multiclass extension is introduced using the Gibbs ..."
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Cited by 5 (2 self)
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We present two graphbased algorithms for multiclass segmentation of highdimensional data. The algorithms use a diffuse interface model based on the GinzburgLandau functional, related to total variation compressed sensing and image processing. A multiclass extension is introduced using the Gibbs simplex, with the functional’s doublewell potential modified to handle the multiclass case. The first algorithm minimizes the functional using a convex splitting numerical scheme. The second algorithm is a uses a graph adaptation of the classical numerical MerrimanBenceOsher (MBO) scheme, which alternates between diffusion and thresholding. We demonstrate the performance of both algorithms experimentally on synthetic data, grayscale and color images, and several benchmark data sets such as MNIST, COIL and WebKB. We also make use of fast numerical solvers for finding the eigenvectors and eigenvalues of the graph Laplacian, and take advantage of the sparsity of the matrix. Experiments indicate that the results are competitive with or better than the current stateoftheart multiclass segmentation algorithms.
Multiclass diffuse interface models for semisupervised learning on graphs
 in Proceedings of the 2th International Conference on Pattern Recognition Applications and Methods. SciTePress
, 2013
"... Abstract: We present a graphbased variational algorithm for multiclass classification of highdimensional data, motivated by total variation techniques. The energy functional is based on a diffuse interface model with a periodic potential. We augment the model by introducing an alternative measure ..."
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Cited by 2 (2 self)
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Abstract: We present a graphbased variational algorithm for multiclass classification of highdimensional data, motivated by total variation techniques. The energy functional is based on a diffuse interface model with a periodic potential. We augment the model by introducing an alternative measure of smoothness that preserves symmetry among the class labels. Through this modification of the standard Laplacian, we construct an efficient multiclass method that allows for sharp transitions between classes. The experimental results demonstrate that our approach is competitive with the state of the art among other graphbased algorithms. 1
Graphbased Semisupervised Learning: Realizing Pointwise Smoothness Probabilistically Yuan Fang † ‡
"... As the central notion in semisupervised learning, smoothness is often realized on a graph representation of the data. In this paper, we study two complementary dimensions of smoothness: its pointwise nature and probabilistic modeling. While no existing graphbased work exploits them in conjunctio ..."
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Cited by 1 (1 self)
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As the central notion in semisupervised learning, smoothness is often realized on a graph representation of the data. In this paper, we study two complementary dimensions of smoothness: its pointwise nature and probabilistic modeling. While no existing graphbased work exploits them in conjunction, we encompass both in a novel framework of Probabilistic Graphbased Pointwise Smoothness (PGP), building upon two foundational models of data closeness and label coupling. This new form of smoothness axiomatizes a set of probability constraints, which ultimately enables class prediction. Theoretically, we provide an error and robustness analysis of PGP. Empirically, we conduct extensive experiments to show the advantages of PGP. 1.
1Multiclass Data Segmentation using Diffuse Interface Methods on Graphs
"... Abstract—We present two graphbased algorithms for multiclass segmentation of highdimensional data on graphs. The algorithms use a diffuse interface model based on the GinzburgLandau functional, related to total variation and graph cuts. A multiclass extension is introduced using the Gibbs simplex ..."
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Cited by 1 (0 self)
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Abstract—We present two graphbased algorithms for multiclass segmentation of highdimensional data on graphs. The algorithms use a diffuse interface model based on the GinzburgLandau functional, related to total variation and graph cuts. A multiclass extension is introduced using the Gibbs simplex, with the functional’s doublewell potential modified to handle the multiclass case. The first algorithm minimizes the functional using a convex splitting numerical scheme. The second algorithm uses a graph adaptation of the classical numerical MerrimanBenceOsher (MBO) scheme, which alternates between diffusion and thresholding. We demonstrate the performance of both algorithms experimentally on synthetic data, image labeling, and several benchmark data sets such as MNIST, COIL and WebKB. We also make use of fast numerical solvers for finding the eigenvectors and eigenvalues of the graph Laplacian, and take advantage of the sparsity of the matrix. Experiments indicate that the results are competitive with or better than the current stateoftheart in multiclass graphbased segmentation algorithms for highdimensional data.
Scaling Graphbased Semi Supervised Learning to Large Number of Labels Using CountMin Sketch
"... Graphbased Semisupervised learning (SSL) algorithms have been successfully used in a large number of applications. These methods classify initially unlabeled nodes by propagating label information over the structure of graph starting from seed nodes. Graphbased SSL algorithms usually scale linea ..."
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Graphbased Semisupervised learning (SSL) algorithms have been successfully used in a large number of applications. These methods classify initially unlabeled nodes by propagating label information over the structure of graph starting from seed nodes. Graphbased SSL algorithms usually scale linearly with the number of distinct labels (m), and require O(m) space on each node. Unfortunately, there exist many applications of practical significance with very large m over large graphs, demanding better space and time complexity. In this paper, we propose MADSketch, a novel graphbased SSL algorithm which compactly stores label distribution on each node using Countmin Sketch, a randomized data structure. We present theoretical analysis showing that under mild conditions, MADSketch can reduce space complexity at each node from O(m) to O(logm), and achieve similar savings in time complexity as well. We support our analysis through experiments on multiple real world datasets. We observe that MADSketch achieves similar performance as existing stateoftheart graphbased SSL algorithms, while requiring smaller memory footprint and at the same time achieving up to 10x speedup. We find that MADSketch is able to scale to datasets with one million labels, which is beyond the scope of existing graphbased SSL algorithms.
Using Unlabeled Data to Improve Inductive Models by Incorporating Transductive Models
"... Abstract—This paper shows how to use labeled and unlabeled data to improve inductive models with the help of transductive models. We proposed a solution for the selftraining scenario. Selftraining is an effective semisupervised wrapper method which can generalize any type of supervised inductive m ..."
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Abstract—This paper shows how to use labeled and unlabeled data to improve inductive models with the help of transductive models. We proposed a solution for the selftraining scenario. Selftraining is an effective semisupervised wrapper method which can generalize any type of supervised inductive model to the semisupervised settings. it iteratively refines a inductive model by bootstrap from unlabeled data. Standard selftraining uses the classifier model(trained on labeled examples) to label and select candidates from the unlabeled training set, which may be problematic since the initial classifier may not be able to provide highly confident predictions as labeled training data is always rare. As a result, it could always suffer from introducing too much wrongly labeled candidates to the labeled training set, which may severely degrades performance. To tackle this problem, we propose a novel selftraining style algorithm which incorporate a graphbased transductive model in the selflabeling process. Unlike standard selftraining, our algorithm utilizes labeled and unlabeled data as a whole to label and select unlabeled examples for training set augmentation. A robust transductive model based on graph markov random walk is proposed, which exploits manifold assumption to output reliable predictions on unlabeled data using noisy labeled examples. The proposed algorithm can greatly minimize the risk of performance degradation due to accumulated noise in the training set. Experiments show that the proposed algorithm can effectively utilize unlabeled data to improve classification performance. Keywords—Inductive model, Transductive model, Semisupervised learning, Markov random walk.
LASS: A Simple Assignment Model with Laplacian Smoothing
"... We consider the problem of learning soft assignments of N items to K categories given two sources of information: an itemcategory similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an item ..."
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We consider the problem of learning soft assignments of N items to K categories given two sources of information: an itemcategory similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an itemitem similarity matrix, which encourages similar items to have similar assignments. We propose a simple quadratic programming model that captures this intuition. We give necessary conditions for its solution to be unique, define an outofsample mapping, and derive a simple, effective training algorithm based on the alternating direction method of multipliers. The model predicts reasonable assignments from even a few similarity values, and can be seen as a generalization of semisupervised learning. It is particularly useful when items naturally belong to multiple categories, as for example when annotating documents with keywords or pictures with tags, with partially tagged items, or when the categories have complex interrelations (e.g. hierarchical) that are unknown. 1
Multiclass SemiSupervised Learning on Graphs using GinzburgLandau Functional Minimization
"... Abstract. We present a graphbased variational algorithm for classification of highdimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms ..."
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Abstract. We present a graphbased variational algorithm for classification of highdimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semisupervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with stateoftheart graphbased multiclass segmentation methods.
Parallel GraphBased SemiSupervised Learning
, 2011
"... Semisupervised learning (SSL) is the process of training decision functions using small amounts of labeled and relatively large amounts of unlabeled data. In many applications, annotating training data is timeconsuming and error prone. Speech recognition is the typical example, which requires larg ..."
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Semisupervised learning (SSL) is the process of training decision functions using small amounts of labeled and relatively large amounts of unlabeled data. In many applications, annotating training data is timeconsuming and error prone. Speech recognition is the typical example, which requires large amounts of meticulously