"Sliced" Subwindow Search: a Sublinear-complexity Solution to the Maximum Rectangle Problem

Considering a 2D matrix of positive and negative numbers, how might one draw a rectangle within it whose contents sum higher than all other rectangles'? This fundamental problem, commonly known the maximum rectangle problem or subwindow search, spans many computational domains. Yet, the problem has not been solved without demanding computational resources at least linearly proportional to the size of the matrix. In this work, we present a new approach to the problem which achieves sublinear time and memory complexities by interpolating between a small amount of equidistant sections of the matrix. Applied to natural images, our solution outperforms the state-of-the-art by achieving an 11x increase in speed and memory efficiency at 99% comparative accuracy. In general, our solution outperforms existing solutions when matrices are sufficiently large and a marginal decrease in accuracy is acceptable, such as in many problems involving natural images. As such, it is well-suited for real-time application and in a variety of computationally hard instances of the maximum rectangle problem.Comment: 8 pages, 7 figure

Large Scale Nearest Neighbor Search - Theories, Algorithms, and Applications

We are witnessing a data explosion era, in which huge data sets of billions or more samples represented by high-dimensional feature vectors can be easily found on the Web, enterprise data centers, surveillance sensor systems, and so on. On these large scale data sets, nearest neighbor search is fundamental for lots of applications including content based search/retrieval, recommendation, clustering, graph and social network research, as well as many other machine learning and data mining problems. Exhaustive search is the simplest and most straightforward way for nearest neighbor search, but it can not scale up to huge data set at the sizes as mentioned above. To make large scale nearest neighbor search practical, we need the online search step to be sublinear in terms of the database size, which means offline indexing is necessary. Moreover, to achieve sublinear search time, we usually need to make some sacrifice on the search accuracy, and hence we can often only obtain approximate nearest neighbor instead of exact nearest neighbor. In other words, by large scale nearest neighbor search, we aim at approximate nearest neighbor search methods with sublinear online search time via offline indexing. To some extent, indexing a vector dataset for (sublinear time) approximate search can be achieved by partitioning the feature space to different regions, and mapping each point to its closet regions. There are different kinds of partition structures, for example, tree based partition, hashing based partition, clustering/quantization based partition, etc. From the viewpoint of how the data partition function is generated, the partition methods can be grouped into two main categories: 1. data independent (random) partition such as locality sensitive hashing, randomized trees/forests methods, etc.; 2. data dependent (optimized) partition, such as compact hashing, quantization based indexing methods, and some tree based methods like kd-tree, pca tree, etc. With the offline indexing/partitioning, online approximate nearest neighbor search usually consists of three steps: locate the query region that the query point falls in, obtain candidates which are the database points in the regions near the query region, and rerank/return candidates. For large scale nearest neighbor search, the key question is: how to design the optimal offline indexing, such that the online search performance is the best, or more specifically, the online search can be as fast as possible, while meeting a required accuracy? In this thesis, we have studied theories, algorithms, systems and applications for (approximate) nearest neighbor search on large scale data sets, for both indexing with random partition and indexing with learning based partition. Our specific main contributions are: 1. We unify various nearest neighbor search methods into the data partition framework, and provide a general formulation of optimal data partition, which supports fastest search speed while satisfying a required search accuracy. The formulation is general, and can be used to explain most existing (sublinear) large scale approximate nearest neighbor search methods. 2. For indexing with data-independent partitions, we have developed theories on their lower and upper bounds of time and space complexity, based on the optimal data partition formulation. The bounds are applicable for a general group of methods called Nearest Neighbor Preferred Hashing and Nearest Neighbor Preferred Partition, including, locality sensitive hashing, random forest, and many other random hashing methods, etc. Moreover, we also extend the theory to study how to choose the parameters for indexing methods with random partitions. 3. For indexing with data-dependent partitions, I have applied the same formulation to develop a joint optimization approach with two important criteria: nearest neighbor preserving and region size balancing. we have applied the joint optimization to different partition structures such as hashing and clustering, and achieved several new nearest neighbor search methods, outperforming (or at least comparable) to state-of-the-art solutions for large scale nearest neighbor search. 4. we have further studied fundamental problems for nearest neighbor search beyond search methods, for example, what is the difficulty of nearest neighbor search on a given data set (independent of search methods)? What data properties affect the difficulty and how? How will the theoretical analysis and algorithm design of large scale nearest neighbor search problem be affected by the data set difficulty? 5. Finally, we have applied our nearest neighbor search methods for practical applications. We focus on the development of large visual search engines using new indexing methods developed in this thesis. The techniques can be applied to other domains with data-intensive applications, and moreover, be extended to other applications beyond visual search engine, such as large scale machine learning, data mining, and social network analysis, etc

Adaptive Learning and Mining for Data Streams and Frequent Patterns

Aquesta tesi està dedicada al disseny d'algorismes de mineria de dades per fluxos de dades que evolucionen en el temps i per l'extracció d'arbres freqüents tancats. Primer ens ocupem de cadascuna d'aquestes tasques per separat i, a continuació, ens ocupem d'elles conjuntament, desenvolupant mètodes de classificació de fluxos de dades que contenen elements que són arbres. En el model de flux de dades, les dades arriben a gran velocitat, i els algorismes que els han de processar tenen limitacions estrictes de temps i espai. En la primera part d'aquesta tesi proposem i mostrem un marc per desenvolupar algorismes que aprenen de forma adaptativa dels fluxos de dades que canvien en el temps. Els nostres mètodes es basen en l'ús de mòduls detectors de canvi i estimadors en els llocs correctes. Proposem ADWIN, un algorisme de finestra lliscant adaptativa, per la detecció de canvi i manteniment d'estadístiques actualitzades, i proposem utilitzar-lo com a caixa negra substituint els comptadors en algorismes inicialment no dissenyats per a dades que varien en el temps. Com ADWIN té garanties teòriques de funcionament, això obre la possibilitat d'ampliar aquestes garanties als algorismes d'aprenentatge i de mineria de dades que l'usin. Provem la nostre metodologia amb diversos mètodes d'aprenentatge com el Naïve Bayes, partició, arbres de decisió i conjunt de classificadors. Construïm un marc experimental per fer mineria amb fluxos de dades que varien en el temps, basat en el programari MOA, similar al programari WEKA, de manera que sigui fàcil pels investigadors de realitzar-hi proves experimentals. Els arbres són grafs acíclics connectats i són estudiats com vincles en molts casos. En la segona part d'aquesta tesi, descrivim un estudi formal dels arbres des del punt de vista de mineria de dades basada en tancats. A més, presentem algorismes eficients per fer tests de subarbres i per fer mineria d'arbres freqüents tancats ordenats i no ordenats. S'inclou una anàlisi de l'extracció de regles d'associació de confiança plena dels conjunts d'arbres tancats, on hem trobat un fenomen interessant: les regles que la seva contrapart proposicional és no trivial, són sempre certes en els arbres a causa de la seva peculiar combinatòria. I finalment, usant aquests resultats en fluxos de dades evolutius i la mineria d'arbres tancats freqüents, hem presentat algorismes d'alt rendiment per fer mineria d'arbres freqüents tancats de manera adaptativa en fluxos de dades que evolucionen en el temps. Introduïm una metodologia general per identificar patrons tancats en un flux de dades, utilitzant la Teoria de Reticles de Galois. Usant aquesta metodologia, desenvolupem un algorisme incremental, un basat en finestra lliscant, i finalment un que troba arbres freqüents tancats de manera adaptativa en fluxos de dades. Finalment usem aquests mètodes per a desenvolupar mètodes de classificació per a fluxos de dades d'arbres.This thesis is devoted to the design of data mining algorithms for evolving data streams and for the extraction of closed frequent trees. First, we deal with each of these tasks separately, and then we deal with them together, developing classification methods for data streams containing items that are trees. In the data stream model, data arrive at high speed, and the algorithms that must process them have very strict constraints of space and time. In the first part of this thesis we propose and illustrate a framework for developing algorithms that can adaptively learn from data streams that change over time. Our methods are based on using change detectors and estimator modules at the right places. We propose an adaptive sliding window algorithm ADWIN for detecting change and keeping updated statistics from a data stream, and use it as a black-box in place or counters or accumulators in algorithms initially not designed for drifting data. Since ADWIN has rigorous performance guarantees, this opens the possibility of extending such guarantees to learning and mining algorithms. We test our methodology with several learning methods as Naïve Bayes, clustering, decision trees and ensemble methods. We build an experimental framework for data stream mining with concept drift, based on the MOA framework, similar to WEKA, so that it will be easy for researchers to run experimental data stream benchmarks. Trees are connected acyclic graphs and they are studied as link-based structures in many cases. In the second part of this thesis, we describe a rather formal study of trees from the point of view of closure-based mining. Moreover, we present efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. We include an analysis of the extraction of association rules of full confidence out of the closed sets of trees, and we have found there an interesting phenomenon: rules whose propositional counterpart is nontrivial are, however, always implicitly true in trees due to the peculiar combinatorics of the structures. And finally, using these results on evolving data streams mining and closed frequent tree mining, we present high performance algorithms for mining closed unlabeled rooted trees adaptively from data streams that change over time. We introduce a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. Using this methodology, we then develop an incremental one, a sliding-window based one, and finally one that mines closed trees adaptively from data streams. We use these methods to develop classification methods for tree data streams.Postprint (published version

Learning task-specific similarity

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 139-147).The right measure of similarity between examples is important in many areas of computer science. In particular it is a critical component in example-based learning methods. Similarity is commonly defined in terms of a conventional distance function, but such a definition does not necessarily capture the inherent meaning of similarity, which tends to depend on the underlying task. We develop an algorithmic approach to learning similarity from examples of what objects are deemed similar according to the task-specific notion of similarity at hand, as well as optional negative examples. Our learning algorithm constructs, in a greedy fashion, an encoding of the data. This encoding can be seen as an embedding into a space, where a weighted Hamming distance is correlated with the unknown similarity. This allows us to predict when two previously unseen examples are similar and, importantly, to efficiently search a very large database for examples similar to a query. This approach is tested on a set of standard machine learning benchmark problems. The model of similarity learned with our algorithm provides and improvement over standard example-based classification and regression. We also apply this framework to problems in computer vision: articulated pose estimation of humans from single images, articulated tracking in video, and matching image regions subject to generic visual similarity.by Gregory Shakhnarovich.Ph.D

Efficient inference and learning in graphical models for multi-organ shape segmentation

This thesis explores the use of discriminatively trained deformable contour models (DCMs) for shape-based segmentation in medical images. We make contributions in two fronts: in the learning problem, where the model is trained from a set of annotated images, and in the inference problem, whose aim is to segment an image given a model. We demonstrate the merit of our techniques in a large X-Ray image segmentation benchmark, where we obtain systematic improvements in accuracy and speedups over the current state-of-the-art. For learning, we formulate training the DCM scoring function as large-margin structured prediction and construct a training objective that aims at giving the highest score to the ground-truth contour configuration. We incorporate a loss function adapted to DCM-based structured prediction. In particular, we consider training with the Mean Contour Distance (MCD) performance measure. Using this loss function during training amounts to scoring each candidate contour according to its Mean Contour Distance to the ground truth configuration. Training DCMs using structured prediction with the standard zero-one loss already outperforms the current state-of-the-art method [Seghers et al. 2007] on the considered medical benchmark [Shiraishi et al. 2000, van Ginneken et al. 2006]. We demonstrate that training with the MCD structured loss further improves over the generic zero-one loss results by a statistically significant amount. For inference, we propose efficient solvers adapted to combinatorial problems with discretized spatial variables. Our contributions are three-fold:first, we consider inference for loopy graphical models, making no assumption about the underlying graph topology. We use an efficient decomposition-coordination algorithm to solve the resulting optimization problem: we decompose the model’s graph into a set of open, chain-structured graphs. We employ the Alternating Direction Method of Multipliers (ADMM) to fix the potential inconsistencies of the individual solutions. Even-though ADMMis an approximate inference scheme, we show empirically that our implementation delivers the exact solution for the considered examples. Second,we accelerate optimization of chain-structured graphical models by using the Hierarchical A∗ search algorithm of [Felzenszwalb & Mcallester 2007] couple dwith the pruning techniques developed in [Kokkinos 2011a]. We achieve a one order of magnitude speedup in average over the state-of-the-art technique based on Dynamic Programming (DP) coupled with Generalized DistanceTransforms (GDTs) [Felzenszwalb & Huttenlocher 2004]. Third, we incorporate the Hierarchical A∗ algorithm in the ADMM scheme to guarantee an efficient optimization of the underlying chain structured subproblems. The resulting algorithm is naturally adapted to solve the loss-augmented inference problem in structured prediction learning, and hence is used during training and inference. In Appendix A, we consider the case of 3D data and we develop an efficientmethod to find the mode of a 3D kernel density distribution. Our algorithm has guaranteed convergence to the global optimum, and scales logarithmically in the volume size by virtue of recursively subdividing the search space. We use this method to rapidly initialize 3D brain tumor segmentation where we demonstrate substantial acceleration with respect to a standard mean-shift implementation. In Appendix B, we describe in more details our extension of the Hierarchical A∗ search algorithm of [Felzenszwalb & Mcallester 2007] to inference on chain-structured graphs.Cette thèse explore l’utilisation des modèles de contours déformables pour la segmentation basée sur la forme des images médicales. Nous apportons des contributions sur deux fronts: dans le problème de l’apprentissage statistique, où le modèle est formé à partir d’un ensemble d’images annotées, et le problème de l’inférence, dont le but est de segmenter une image étant donnée un modèle. Nous démontrons le mérite de nos techniques sur une grande base d’images à rayons X, où nous obtenons des améliorations systématiques et des accélérations par rapport à la méthode de l’état de l’art. Concernant l’apprentissage, nous formulons la formation de la fonction de score des modèles de contours déformables en un problème de prédiction structurée à grande marge et construisons une fonction d’apprentissage qui vise à donner le plus haut score à la configuration vérité-terrain. Nous intégrons une fonction de perte adaptée à la prédiction structurée pour les modèles de contours déformables. En particulier, nous considérons l’apprentissage avec la mesure de performance consistant en la distance moyenne entre contours, comme une fonction de perte. L’utilisation de cette fonction de perte au cours de l’apprentissage revient à classer chaque contour candidat selon sa distance moyenne du contour vérité-terrain. Notre apprentissage des modèles de contours déformables en utilisant la prédiction structurée avec la fonction zéro-un de perte surpasse la méthode [Seghers et al. 2007] de référence sur la base d’images médicales considérée [Shiraishi et al. 2000, van Ginneken et al. 2006]. Nous démontrons que l’apprentissage avec la fonction de perte de distance moyenne entre contours améliore encore plus les résultats produits avec l’apprentissage utilisant la fonction zéro-un de perte et ce d’une quantité statistiquement significative.Concernant l’inférence, nous proposons des solveurs efficaces et adaptés aux problèmes combinatoires à variables spatiales discrétisées. Nos contributions sont triples: d’abord, nous considérons le problème d’inférence pour des modèles graphiques qui contiennent des boucles, ne faisant aucune hypothèse sur la topologie du graphe sous-jacent. Nous utilisons un algorithme de décomposition-coordination efficace pour résoudre le problème d’optimisation résultant: nous décomposons le graphe du modèle en un ensemble de sous-graphes en forme de chaines ouvertes. Nous employons la Méthode de direction alternée des multiplicateurs (ADMM) pour réparer les incohérences des solutions individuelles. Même si ADMM est une méthode d’inférence approximative, nous montrons empiriquement que notre implémentation fournit une solution exacte pour les exemples considérés. Deuxièmement, nous accélérons l’optimisation des modèles graphiques en forme de chaîne en utilisant l’algorithme de recherche hiérarchique A* [Felzenszwalb & Mcallester 2007] couplé avec les techniques d’élagage développés dans [Kokkinos 2011a]. Nous réalisons une accélération de 10 fois en moyenne par rapport à l’état de l’art qui est basé sur la programmation dynamique (DP) couplé avec les transformées de distances généralisées [Felzenszwalb & Huttenlocher 2004]. Troisièmement, nous intégrons A* dans le schéma d’ADMM pour garantir une optimisation efficace des sous-problèmes en forme de chaine. En outre, l’algorithme résultant est adapté pour résoudre les problèmes d’inférence augmentée par une fonction de perte qui se pose lors de l’apprentissage de prédiction des structure, et est donc utilisé lors de l’apprentissage et de l’inférence. [...

Open-Ended Learning of Visual and Multi-Modal Patterns

A common trend in machine learning and pattern classification research is the exploitation of massive amounts of information in order to achieve an increase in performance. In particular, learning from huge collections of data obtained from the web, and using multiple features generated from different sources, have led to significantly boost of performance on problems that have been considered very hard for several years. In this thesis, we present two ways of using these information to build learning systems with robust performance and some degrees of autonomy. These ways are Cue Integration and Cue Exploitation, and constitute the two building blocks of this thesis. In the first block, we introduce several algorithms to answer the research question on how to integrate optimally multiple features. We first present a simple online learning framework which is a wrapper algorithm based on the high-level integration approach in the cue integration literature. It can be implemented with existing online learning algorithms, and preserves the theoretical properties of the algorithms being used. We then extend the Multiple Kernel Learning (MKL) framework, where each feature is converted into a kernel and the system learns the cue integration classifier by solving a joint optimization problem. To make the problem practical, We have designed two new regularization functions making it possible to optimize the problem efficiently. This results in the first online method for MKL. We also show two algorithms to solve the batch problem of MKL. Both of them have a guaranteed convergence rate. These approaches achieve state-of-the-art performance on several standard benchmark datasets, and are order of magnitude faster than other MKL solvers. In the second block, We present two examples on how to exploit information between different sources, in order to reduce the effort of labeling a large amount of training data. The first example is an algorithm to learn from partially annotated data, where each data point is tagged with a few possible labels. We show that it is possible to train a face classification system from data gathered from Internet, without any human labeling, but generating in an automatic way possible lists of labels from the captions of the images. Another example is under the transfer learning setting. The system uses existing models from potentially correlated tasks as experts, and transfers their outputs over the new incoming samples, of a new learning task where very few labeled data are available, to boost the performance