Statistical learning techniques for text categorization with sparse labeled data

Many applications involve learning a supervised classifier from very few explicitly labeled training examples, since the cost of manually labeling the training data is often prohibitively high. For instance, we expect a good classifier to learn our interests from a few example books or movies we like, and recommend similar ones in the future, or we expect a search engine to give more personalized search results based on whatever little it learned about our past queries and clicked documents. There is thus a need for classification techniques capable of learning from sparse labeled data, by exploiting additional information about the classification task at hand (e.g., background knowledge) or by employing more sophisticated features (e.g., n-gram sequences, trees, graphs). In this thesis, we focus on two approaches for overcoming the bottleneck of sparse labeled data. We first propose the Inductive/Transductive Latent Model (ILM/TLM), which is a new generative model for text documents. ILM/TLM has various building blocks designed to facilitate the integration of background knowledge (e.g., unlabeled documents, ontologies of concepts, encyclopedia) into the process of learning from small training data. Our method can be used for inductive and transductive learning and achieves significant gains over state-of-the-art methods for very small training sets. Second, we propose Structured Logistic Regression (SLR), which is a new coordinate-wise gradient ascent technique for learning logistic regression in the space of all (word or character) sequences in the training data. SLR exploits the inherent structure of the n-gram feature space in order to automatically provide a compact set of highly discriminative n-gram features. Our detailed experimental study shows that while SLR achieves similar classification results to those of the state-of-the-art methods (which use all n-gram features given explicitly), it is more than an order of magnitude faster than its opponents. The techniques presented in this thesis can be used to advance the technologies for automatically and efficiently building large training sets, therefore reducing the need for spending human computation on this task.Viele Anwendungen benutzen Klassifikatoren, die auf dünn gesäten Trainingsdaten lernen müssen, da es oft aufwändig ist, Trainingsdaten zur Verfügung zu stellen. Ein Beispiel für solche Anwendungen sind Empfehlungssysteme, die auf der Basis von sehr wenigen Büchern oder Filmen die Interessen des Benutzers erraten müssen, um ihm ähnliche Bücher oder Filme zu empfehlen. Ein anderes Beispiel sind Suchmaschinen, die sich auf den Benutzer einzustellen versuchen, auch wenn sie bisher nur sehr wenig Information über den Benutzer in Form von gestellten Anfragen oder geklickten Dokumenten besitzen. Wir benötigen also Klassifikationstechniken, die von dünn gesäten Trainingsdaten lernen können. Dies kann geschehen, indem zusätzliche Information über die Klassifikationsaufgabe ausgenutzt wird (z.B. mit Hintergrundwissen) oder indem raffiniertere Merkmale verwendet werden (z.B. n-Gram-Folgen, Bäume oder Graphen). In dieser Arbeit stellen wir zwei Ansätze vor, um das Problem der dünn gesäten Trainingsdaten anzugehen. Als erstes schlagen wir das Induktiv-Transduktive Latente Modell (ILM/TLM) vor, ein neues generatives Modell für Text-Dokumente. Das ILM/TLM verfügt über mehrere Komponenten, die es erlauben, Hintergrundwissen (wie z.B. nicht Klassifizierte Dokumente, Konzeptontologien oder Enzyklopädien) in den Lernprozess mit einzubeziehen. Diese Methode kann sowohl für induktives als auch für transduktives Lernen eingesetzt werden. Sie schlägt die modernsten Alternativmethoden signifikant bei dünn gesäten Trainingsdaten. Zweitens schlagen wir Strukturierte Logistische Regression (SLR) vor, ein neues Gradientenverfahren zum koordinatenweisen Lernen von logistischer Regression im Raum aller Wortfolgen oder Zeichenfolgen in den Trainingsdaten. SLR nutzt die inhärente Struktur des n-Gram-Raums aus, um automatisch hoch-diskriminative Merkmale zu finden. Unsere detaillierten Experimente zeigen, dass SLR ähnliche Ergebnisse erzielt wie die modernsten Konkurrenzmethoden, allerdings dabei um mehr als eine Größenordnung schneller ist. Die in dieser Arbeit vorgestellten Techniken verbessern das Maschinelle Lernen auf dünn gesäten Trainingsdaten und verringern den Bedarf an manueller Arbeit

Minimum Density Hyperplanes

Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data. We propose a novel hyperplane classifier for clustering and semi-supervised classification which is motivated by this objective. The proposed minimum density hyperplane minimises the integral of the empirical probability density function along it, thereby avoiding intersection with high density clusters. We show that the minimum density and the maximum margin hyperplanes are asymptotically equivalent, thus linking this approach to maximum margin clustering and semi-supervised support vector classifiers. We propose a projection pursuit formulation of the associated optimisation problem which allows us to find minimum density hyperplanes efficiently in practice, and evaluate its performance on a range of benchmark datasets. The proposed approach is found to be very competitive with state of the art methods for clustering and semi-supervised classification

Exploiting structure defined by data in machine learning: some new analyses

This thesis offers some new analyses and presents some new methods for learning in the context of exploiting structure defined by data – for example, when a data distribution has a submanifold support, exhibits cluster structure or exists as an object such as a graph. 1. We present a new PAC-Bayes analysis of learning in this context, which is sharp and in some ways presents a better solution than uniform convergence methods. The PAC-Bayes prior over a hypothesis class is defined in terms of the unknown true risk and smoothness of hypotheses w.r.t. the unknown data-generating distribution. The analysis is “localized” in the sense that complexity of the model enters not as the complexity of an entire hypothesis class, but focused on functions of ultimate interest. Such bounds are derived for various algorithms including SVMs. 2. We consider an idea similar to the p-norm Perceptron for building classifiers on graphs. We define p-norms on the space of functions over graph vertices and consider interpolation using the pnorm as a smoothness measure. The method exploits cluster structure and attains a mistake bound logarithmic in the diameter, compared to a linear lower bound for standard methods. 3. Rademacher complexity is related to cluster structure in data, quantifying the notion that when data clusters we can learn well with fewer examples. In particular we relate transductive learning to cluster structure in the empirical resistance metric. 4. Typical methods for learning over a graph do not scale well in the number of data points – often a graph Laplacian must be inverted which becomes computationally intractable for large data sets. We present online algorithms which, by simplifying the graph in principled way, are able to exploit the structure while remaining computationally tractable for large datasets. We prove state-of-the-art performance guarantees

Adaptive Learning Algorithms for Non-stationary Data

With the wide availability of large amounts of data and acute need for extracting useful information from such data, intelligent data analysis has attracted great attention and contributed to solving many practical tasks, ranging from scientific research, industrial process and daily life. In many cases the data evolve over time or change from one domain to another. The non-stationary nature of the data brings a new challenge for many existing learning algorithms, which are based on the stationary assumption. This dissertation addresses three crucial problems towards the effective handling of non-stationary data by investigating systematic methods for sample reweighting. Sample reweighting is a problem that infers sample-dependent weights for a data collection to match another data collection which exhibits distributional difference. It is known as the density-ratio estimation problem and the estimation results can be used in several machine learning tasks. This research proposes a set of methods for distribution matching by developing novel density-ratio methods that incorporate the characters of different non-stationary data analysis tasks. The contributions are summarized below. First, for the domain adaptation of classification problems a novel discriminative density-ratio method is proposed. This approach combines three learning objectives: minimizing generalized risk on the reweighted training data, minimizing class-wise distribution discrepancy and maximizing the separation margin on the test data. To solve the discriminative density-ratio problem, two algorithms are presented on the basis of a block coordinate update optimization scheme. Experiments conducted on different domain adaptation scenarios demonstrate the effectiveness of the proposed algorithms. Second, for detecting novel instances in the test data a locally-adaptive kernel density-ratio method is proposed. While traditional novelty detection algorithms are limited to detect either emerging novel instances which are completely new, or evolving novel instances whose distribution are different from previously-seen ones, the proposed algorithm builds on the success of the idea of using density ratio as a measure of evolving novelty and augments with structural information of each data instance's neighborhood. This makes the estimation of density ratio more reliable, and results in detection of emerging as well as evolving novelties. In addition, the proposed locally-adaptive kernel novelty detection method is applied in the social media analysis and shows favorable performance over other existing approaches. As the time continuity of social media streams, the novelty is usually characterized by the combination of emerging and evolving. One reason is the existence of large common vocabularies between different topics. Another reason is that there are high possibilities of topics being continuously discussed in sequential batch of collections, but showing different level of intensity. Thus, the presented novelty detection algorithm demonstrates its effectiveness in the social media data analysis. Lastly, an auto-tuning method for the non-parametric kernel mean matching estimator is presented. It introduces a new quality measure for evaluating the goodness of distribution matching which reflects the normalized mean square error of estimates. The proposed quality measure does not depend on the learner in the following step and accordingly allows the model selection procedures for importance estimation and prediction model learning to be completely separated

Semi-supervised machine learning techniques for classification of evolving data in pattern recognition

The amount of data recorded and processed over recent years has increased exponentially. To create intelligent systems that can learn from this data, we need to be able to identify patterns hidden in the data itself, learn these pattern and predict future results based on our current observations. If we think about this system in the context of time, the data itself evolves and so does the nature of the classification problem. As more data become available, different classification algorithms are suitable for a particular setting. At the beginning of the learning cycle when we have a limited amount of data, online learning algorithms are more suitable. When truly large amounts of data become available, we need algorithms that can handle large amounts of data that might be only partially labeled as a result of the bottleneck in the learning pipeline from human labeling of the data. An excellent example of evolving data is gesture recognition, and it is present throughout our work. We need a gesture recognition system to work fast and with very few examples at the beginning. Over time, we are able to collect more data and the system can improve. As the system evolves, the user expects it to work better and not to have to become involved when the classifier is unsure about decisions. This latter situation produces additional unlabeled data. Another example of an application is medical classification, where experts’ time is a rare resource and the amount of received and labeled data disproportionately increases over time. Although the process of data evolution is continuous, we identify three main discrete areas of contribution in different scenarios. When the system is very new and not enough data are available, online learning is used to learn after every single example and to capture the knowledge very fast. With increasing amounts of data, offline learning techniques are applicable. Once the amount of data is overwhelming and the teacher cannot provide labels for all the data, we have another setup that combines labeled and unlabeled data. These three setups define our areas of contribution; and our techniques contribute in each of them with applications to pattern recognition scenarios, such as gesture recognition and sketch recognition. An online learning setup significantly restricts the range of techniques that can be used. In our case, the selected baseline technique is the Evolving TS-Fuzzy Model. The semi-supervised aspect we use is a relation between rules created by this model. Specifically, we propose a transductive similarity model that utilizes the relationship between generated rules based on their decisions about a query sample during the inference time. The activation of each of these rules is adjusted according to the transductive similarity, and the new decision is obtained using the adjusted activation. We also propose several new variations to the transductive similarity itself. Once the amount of data increases, we are not limited to the online learning setup, and we can take advantage of the offline learning scenario, which normally performs better than the online one because of the independence of sample ordering and global optimization with respect to all samples. We use generative methods to obtain data outside of the training set. Specifically, we aim to improve the previously mentioned TS Fuzzy Model by incorporating semi-supervised learning in the offline learning setup without unlabeled data. We use the Universum learning approach and have developed a method called UFuzzy. This method relies on artificially generated examples with high uncertainty (Universum set), and it adjusts the cost function of the algorithm to force the decision boundary to be close to the Universum data. We were able to prove the hypothesis behind the design of the UFuzzy classifier that Universum learning can improve the TS Fuzzy Model and have achieved improved performance on more than two dozen datasets and applications. With increasing amounts of data, we use the last scenario, in which the data comprises both labeled data and additional non-labeled data. This setting is one of the most common ones for semi-supervised learning problems. In this part of our work, we aim to improve the widely popular tecjniques of self-training (and its successor help-training) that are both meta-frameworks over regular classifier methods but require probabilistic representation of output, which can be hard to obtain in the case of discriminative classifiers. Therefore, we develop a new algorithm that uses the modified active learning technique Query-by-Committee (QbC) to sample data with high certainty from the unlabeled set and subsequently embed them into the original training set. Our new method allows us to achieve increased performance over both a range of datasets and a range of classifiers. These three works are connected by gradually relaxing the constraints on the learning setting in which we operate. Although our main motivation behind the development was to increase performance in various real-world tasks (gesture recognition, sketch recognition), we formulated our work as general methods in such a way that they can be used outside a specific application setup, the only restriction being that the underlying data evolve over time. Each of these methods can successfully exist on its own. The best setting in which they can be used is a learning problem where the data evolve over time and it is possible to discretize the evolutionary process. Overall, this work represents a significant contribution to the area of both semi-supervised learning and pattern recognition. It presents new state-of-the-art techniques that overperform baseline solutions, and it opens up new possibilities for future research

Isometry and convexity in dimensionality reduction

The size of data generated every year follows an exponential growth. The number of data points as well as the dimensions have increased dramatically the past 15 years. The gap between the demand from the industry in data processing and the solutions provided by the machine learning community is increasing. Despite the growth in memory and computational power, advanced statistical processing on the order of gigabytes is beyond any possibility. Most sophisticated Machine Learning algorithms require at least quadratic complexity. With the current computer model architecture, algorithms with higher complexity than linear O(N) or O(N logN) are not considered practical. Dimensionality reduction is a challenging problem in machine learning. Often data represented as multidimensional points happen to have high dimensionality. It turns out that the information they carry can be expressed with much less dimensions. Moreover the reduced dimensions of the data can have better interpretability than the original ones. There is a great variety of dimensionality reduction algorithms under the theory of Manifold Learning. Most of the methods such as Isomap, Local Linear Embedding, Local Tangent Space Alignment, Diffusion Maps etc. have been extensively studied under the framework of Kernel Principal Component Analysis (KPCA). In this dissertation we study two current state of the art dimensionality reduction methods, Maximum Variance Unfolding (MVU) and Non-Negative Matrix Factorization (NMF). These two dimensionality reduction methods do not fit under the umbrella of Kernel PCA. MVU is cast as a Semidefinite Program, a modern convex nonlinear optimization algorithm, that offers more flexibility and power compared to iv KPCA. Although MVU and NMF seem to be two disconnected problems, we show that there is a connection between them. Both are special cases of a general nonlinear factorization algorithm that we developed. Two aspects of the algorithms are of particular interest: computational complexity and interpretability. In other words computational complexity answers the question of how fast we can find the best solution of MVU/NMF for large data volumes. Since we are dealing with optimization programs, we need to find the global optimum. Global optimum is strongly connected with the convexity of the problem. Interpretability is strongly connected with local isometry1 that gives meaning in relationships between data points. Another aspect of interpretability is association of data with labeled information. The contributions of this thesis are the following: 1. MVU is modified so that it can scale more efficient. Results are shown on 1 million speech datasets. Limitations of the method are highlighted. 2. An algorithm for fast computations for the furthest neighbors is presented for the first time in the literature. 3. Construction of optimal kernels for Kernel Density Estimation with modern convex programming is presented. For the first time we show that the Leave One Cross Validation (LOOCV) function is quasi-concave. 4. For the first time NMF is formulated as a convex optimization problem 5. An algorithm for the problem of Completely Positive Matrix Factorization is presented. 6. A hybrid algorithm of MVU and NMF the isoNMF is presented combining advantages of both methods. 7. The Isometric Separation Maps (ISM) a variation of MVU that contains classification information is presented. 8. Large scale nonlinear dimensional analysis on the TIMIT speech database is performed. 9. A general nonlinear factorization algorithm is presented based on sequential convex programming. Despite the efforts to scale the proposed methods up to 1 million data points in reasonable time, the gap between the industrial demand and the current state of the art is still orders of magnitude wide.Ph.D.Committee Chair: David Anderson; Committee Co-Chair: Alexander Gray; Committee Member: Anthony Yezzi; Committee Member: Hongyuan Zha; Committee Member: Justin Romberg; Committee Member: Ronald Schafe