Sparse Learning Under Regularization Framework
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Sparse Learning Under Regularization Framework
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Author : Haiqin Yang
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2011-04
Sparse Learning Under Regularization Framework written by Haiqin Yang and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04 with categories.
Regularization is a dominant theme in machine learning and statistics due to its prominent ability in providing an intuitive and principled tool for learning from high-dimensional data. As large-scale learning applications become popular, developing efficient algorithms and parsimonious models become promising and necessary for these applications. Aiming at solving large-scale learning problems, this book tackles the key research problems ranging from feature selection to learning with mixed unlabeled data and learning data similarity representation. More specifically, we focus on the problems in three areas: online learning, semi-supervised learning, and multiple kernel learning. The proposed models can be applied in various applications, including marketing analysis, bioinformatics, pattern recognition, etc.
Sparsity In Machine Learning
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Author : Driss Lahlou Kitane
language : en
Publisher:
Release Date : 2022
Sparsity In Machine Learning written by Driss Lahlou Kitane and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Integer optimization is a highly effective tool in the conception of methods to tackle sparsity. It offers a rigorous framework to build sparse models and has proved to provide more accurate and sparse models than other approaches including the ones using sparsity-inducing regularization norms. This thesis focuses on the application of integer optimization to address sparsity problems.
Unsupervised Feature Learning Via Sparse Hierarchical Representations
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Author : Honglak Lee
language : en
Publisher: Stanford University
Release Date : 2010
Unsupervised Feature Learning Via Sparse Hierarchical Representations written by Honglak Lee and has been published by Stanford University this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good feature representation of the data, and having a poor representation can severely limit the performance of learning algorithms. These feature representations are often hand-designed, require significant amounts of domain knowledge and human labor, and do not generalize well to new domains. To address these issues, I will present machine learning algorithms that can automatically learn good feature representations from unlabeled data in various domains, such as images, audio, text, and robotic sensors. Specifically, I will first describe how efficient sparse coding algorithms --- which represent each input example using a small number of basis vectors --- can be used to learn good low-level representations from unlabeled data. I also show that this gives feature representations that yield improved performance in many machine learning tasks. In addition, building on the deep learning framework, I will present two new algorithms, sparse deep belief networks and convolutional deep belief networks, for building more complex, hierarchical representations, in which more complex features are automatically learned as a composition of simpler ones. When applied to images, this method automatically learns features that correspond to objects and decompositions of objects into object-parts. These features often lead to performance competitive with or better than highly hand-engineered computer vision algorithms in object recognition and segmentation tasks. Further, the same algorithm can be used to learn feature representations from audio data. In particular, the learned features yield improved performance over state-of-the-art methods in several speech recognition tasks.
Sparse Learning For Model Optimization
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Author : Haichuan Yang
language : en
Publisher:
Release Date : 2020
Sparse Learning For Model Optimization written by Haichuan Yang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
"Sparsity has been utilized in many different areas such as machine learning, data compression, and signal processing. It is also one of the most intuitive principles in designing effective machine learning models. Machine learning seeks the best mathematical model in terms of a certain loss function, and sparse learning tries to find the best solution which meets the given sparsity constraint. In this thesis, we apply sparse learning to several different model optimization problems, including traditional shallow learning with convex objectives and recent deep learning applications. Deep Neural Networks (DNNs) have shown superior performance in a bunch of tasks. However, it requires orders of magnitude more computation and energy for inference. Therefore, traditional DNNs lack the practicality in highly resourceconstrained environments such as smart-phones and wearable devices. A practical problem is maximizing the accuracy of the DNN which satisfies the resource consumption budget. We use sparse learning to formulate and solve such problems. Specifically, our formulations optimize DNNs constrained by model size and inference energy. For model size constraint, we allow different layers to have different sparsity and bitwidth, and jointly optimize them within a unified optimization problem. For energy-constrained compression, we first construct the energy model in terms of the layer-wise sparsities, and then use it to formulate sparse learning problems. Experimental results validate the effectiveness of our methods by comparing with state-of-the-art model compression baselines. We demonstrate that sparse learning can be used to build a general framework for optimizing different machine learning models"--Pages xiii-xiv.
Machine Learning And Knowledge Discovery In Databases
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Author : Jos L. Balc Zar
language : en
Publisher:
Release Date : 2011-03-13
Machine Learning And Knowledge Discovery In Databases written by Jos L. Balc Zar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-13 with Data mining categories.
A Family Of Sparsity Promoting Gradient Descent Algorithms Based On Sparse Signal Recovery
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Author : Ching-Hua Lee
language : en
Publisher:
Release Date : 2020
A Family Of Sparsity Promoting Gradient Descent Algorithms Based On Sparse Signal Recovery written by Ching-Hua Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Sparsity has played an important role in numerous signal processing systems. By leveraging sparse representations of signals, many batch estimation algorithms and methods that are efficient, robust, and effective for practical engineering problems have been developed. However, gradient descent-based approaches that are less computationally expensive have become essential to the development of modern machine learning systems, especially the deep neural networks (DNNs). This dissertation examines how we can incorporate sparsity principles into gradient-based learning algorithms, in both signal processing and machine learning applications, for improved estimation and optimization performance. On the signal processing side, we study how to take advantage of sparsity in the system response for improving the convergence rate of the least mean square (LMS) family of adaptive filters, which are derived from using gradient descent on the mean square error objective function. Based on iterative reweighting sparse signal recovery (SSR) techniques, we propose a novel framework for deriving a class of sparsity-aware LMS algorithms by adopting an affine scaling transformation (AST) methodology in the algorithm design process. Sparsity-promoting LMS (SLMS) and Sparsity-promoting Normalized LMS (SNLMS) algorithms are introduced, which can take advantage of, though do not strictly enforce, the sparsity of the underlying system if it already exists for convergence speedup. In addition, the reweighting-AST framework is applied to the conjugate gradient (CG) class of adaptive algorithms, which in general demonstrate a much higher convergence rate than the LMS family. The resulting Sparsity-promoting CG (SCG) algorithm also demonstrates improved convergence characteristics for sparse system identification. Finally, the proposed algorithms are applied to the real-world problem of acoustic feedback reduction encountered in hearing aids. On the machine learning side, we investigate how to exploit the SSR techniques in gradient-based optimization algorithms for learning compact representations in nonlinear estimation tasks, especially with overparameterized models. In particular, the reweighting-AST framework is utilized in the context of estimating a regularized solution exhibiting some desired properties such as sparsity without having to incorporate a regularization penalty. The resulting algorithms in general have a weighted gradient term in the update equation where the weighting matrix provides certain implicit regularization capabilities. We start by establishing a general framework that can possibly extend to various regularizers and then focus on the sparsity regularization aspect. As notable applications of nonlinear model sparsification, we propose i) Sparsity-promoting Stochastic Gradient Descent (SSGD) algorithms for DNN compression and ii) Sparsity-promoting Kernel LMS (SKLMS) and Sparsity-promoting Kernel NLMS (SKNLMS) algorithms for dictionary pruning in kernel methods.
Low Rank Regularization For High Dimensional Sparse Conjunctive Feature Spaces In Information Extraction
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Author : Audi Primadhanty
language : en
Publisher:
Release Date : 2018
Low Rank Regularization For High Dimensional Sparse Conjunctive Feature Spaces In Information Extraction written by Audi Primadhanty and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
One of the challenges in Natural Language Processing (NLP) is the unstructured nature of texts, in which useful information is not easily identifiable. Information Extraction (IE) aims to alleviate it by enabling automatic extraction of structured information from such text sources. The resulting structured information will facilitate easier querying, organizing, and analyzing of data from texts. In this thesis, we are interested in two IE related tasks: (i) named entity classification and (ii) template filling. Specifically, this thesis examines the problem of learning classifiers of text spans and explore its application for extracting named entities and template slot-fillers. In general, our goal is to construct a method to learn classifiers that: (i) require less supervision, (ii) work well with high-dimensional sparse feature spaces and (iii) are able to classify unseen items (i.e. named entities/slot-fillers not observed in training data). The key idea of our contribution is the utilization of unseen conjunctive features. A conjunctive feature is a combination of features from different feature sets. For example, to classify a phrase, one might have one feature set for the context and another set for the phrase itself. When learning a classifier, only a factor of these conjunctive features will be observed in the training set, leaving the rest (i.e. unseen features) unusable for predicting items in test time. We hypothesize that utilizing such unseen conjunctions is useful to address all of the aspects of the goal. We develop a general regularization framework specifically designed for sparse conjunctive feature spaces. Our strategy is based on employing tensors to represent the conjunctive feature space, and forcing the model to induce low-dimensional embeddings of the feature vectors via low-rank regularization on the tensor parameters. Such compressed representation will help prediction by generalizing to novel examples where most of the conjunctions will be unseen in the training set. We conduct experiments on learning named entity classifiers and template filling, focusing on extracting unseen items. We show that when learning classifiers under minimal supervision, our approach is more effective in controlling model capacity than standard techniques for linear classification.
Study On Efficient Sparse And Low Rank Optimization And Its Applications
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Author : Jian Lou
language : en
Publisher:
Release Date : 2018
Study On Efficient Sparse And Low Rank Optimization And Its Applications written by Jian Lou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Algorithms categories.
Sparse and low-rank models have been becoming fundamental machine learning tools and have wide applications in areas including computer vision, data mining, bioinformatics and so on. It is of vital importance, yet of great difficulty, to develop efficient optimization algorithms for solving these models, especially under practical design considerations of computational, communicational and privacy restrictions for ever-growing larger scale problems. This thesis proposes a set of new algorithms to improve the efficiency of the sparse and low-rank models optimization. First, facing a large number of data samples during training of empirical risk minimization (ERM) with structured sparse regularization, the gradient computation part of the optimization can be computationally expensive and becomes the bottleneck. Therefore, I propose two gradient efficient optimization algorithms to reduce the total or per-iteration computational cost of the gradient evaluation step, which are new variants of the widely used generalized conditional gradient (GCG) method and incremental proximal gradient (PG) method, correspondingly. In detail, I propose a novel algorithm under GCG framework that requires optimal count of gradient evaluations as proximal gradient. I also propose a refined variant for a type of gauge regularized problem, where approximation techniques are allowed to further accelerate linear subproblem computation. Moreover, under the incremental proximal gradient framework, I propose to approximate the composite penalty by its proximal average under incremental gradient framework, so that a trade-off is made between precision and efficiency. Theoretical analysis and empirical studies show the efficiency of the proposed methods. Furthermore, the large data dimension (e.g. the large frame size of high-resolution image and video data) can lead to high per-iteration computational complexity, thus results into poor-scalability of the optimization algorithm from practical perspective. In particular, in spectral k-support norm regularized robust low-rank matrix and tensor optimization, traditional proximal map based alternating direction method of multipliers (ADMM) requires to evaluate a super-linear complexity subproblem in each iteration. I propose a set of per-iteration computational efficient alternatives to reduce the cost to linear and nearly linear with respect to the input data dimension for matrix and tensor case, correspondingly. The proposed algorithms consider the dual objective of the original problem that can take advantage of the more computational efficient linear oracle of the spectral k-support norm to be evaluated. Further, by studying the sub-gradient of the loss of the dual objective, a line-search strategy is adopted in the algorithm to enable it to adapt to the Holder smoothness. The overall convergence rate is also provided. Experiments on various computer vision and image processing applications demonstrate the superior prediction performance and computation efficiency of the proposed algorithm. In addition, since machine learning datasets often contain sensitive individual information, privacy-preserving becomes more and more important during sparse optimization. I provide two differentially private optimization algorithms under two common large-scale machine learning computing contexts, i.e., distributed and streaming optimization, correspondingly. For the distributed setting, I develop a new algorithm with 1) guaranteed strict differential privacy requirement, 2) nearly optimal utility and 3) reduced uplink communication complexity, for a nearly unexplored context with features partitioned among different parties under privacy restriction. For the streaming setting, I propose to improve the utility of the private algorithm by trading the privacy of distant input instances, under the differential privacy restriction. I show that the proposed method can either solve the private approximation function by a projected gradient update for projection-friendly constraints, or by a conditional gradient step for linear oracle-friendly constraint, both of which improve the regret bound to match the nonprivate optimal counterpart.
Electromagnetic Brain Imaging
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Author : Kensuke Sekihara
language : en
Publisher: Springer
Release Date : 2015-02-20
Electromagnetic Brain Imaging written by Kensuke Sekihara and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-20 with Medical categories.
This graduate level textbook provides a coherent introduction to the body of main-stream algorithms used in electromagnetic brain imaging, with specific emphasis on novel Bayesian algorithms. It helps readers to more easily understand literature in biomedical engineering and related fields and be ready to pursue research in either the engineering or the neuroscientific aspects of electromagnetic brain imaging. This textbook will not only appeal to graduate students but all scientists and engineers engaged in research on electromagnetic brain imaging.
Regularization Optimization Kernels And Support Vector Machines
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Author : Johan A.K. Suykens
language : en
Publisher: CRC Press
Release Date : 2014-10-23
Regularization Optimization Kernels And Support Vector Machines written by Johan A.K. Suykens and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-23 with Computers categories.
Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vecto