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Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning  [PDF]
Dan Stowell,Mark D. Plumbley
PeerJ , 2015, DOI: 10.7717/peerj.488
Abstract: Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC) features which represent a manually-designed summary of spectral information. However, recent work in machine learning has demonstrated that features learnt automatically from data can often outperform manually-designed feature transforms. Feature learning can be performed at large scale and “unsupervised”, meaning it requires no manual data labelling, yet it can improve performance on “supervised” tasks such as classification. In this work we introduce a technique for feature learning from large volumes of bird sound recordings, inspired by techniques that have proven useful in other domains. We experimentally compare twelve different feature representations derived from the Mel spectrum (of which six use this technique), using four large and diverse databases of bird vocalisations, classified using a random forest classifier. We demonstrate that in our classification tasks, MFCCs can often lead to worse performance than the raw Mel spectral data from which they are derived. Conversely, we demonstrate that unsupervised feature learning provides a substantial boost over MFCCs and Mel spectra without adding computational complexity after the model has been trained. The boost is particularly notable for single-label classification tasks at large scale. The spectro-temporal activations learned through our procedure resemble spectro-temporal receptive fields calculated from avian primary auditory forebrain. However, for one of our datasets, which contains substantial audio data but few annotations, increased performance is not discernible. We study the interaction between dataset characteristics and choice of feature representation through further empirical analysis.
Sleep Stage Classification Using Unsupervised Feature Learning  [PDF]
Martin L?ngkvist,Lars Karlsson,Amy Loutfi
Advances in Artificial Neural Systems , 2012, DOI: 10.1155/2012/107046
Abstract: Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets. 1. Introduction One of the main challenges in sleep stage classification is to isolate features in multivariate time-series data which can be used to correctly identify and thereby automate the annotation process to generate sleep hypnograms. In the current absence of a set of universally applicable features, typically a two-stage process is required before training a sleep stage algorithm, namely, feature extraction and feature selection [1–9]. In other domains which share similar challenges, an alternative to using hand-tailored feature representations derived from expert knowledge is to apply unsupervised feature learning techniques, where the feature representations are learned from unlabeled data. This not only enables the discovery of new useful feature representations that a human expert might not be aware of, which in turn could lead to a better understanding of the sleep process and present a way of exploiting massive amounts of unlabeled data. Unsupervised feature learning and in particular deep learning [10–15] propose ways for training the weight matrices in each layer in an unsupervised fashion as a preprocessing step before training the whole network. This has proven to give good results in other areas such as vision tasks [10], object recognition [16], motion capture data [17], speech recognition [18], and bacteria identification [19]. This work presents a new approach to the automatic sleep staging problem. The main focus is to learn meaningful feature representations from unlabeled sleep data. A dataset of 25 subjects consisting of electroencephalography (EEG) of brain activity, electrooculography (EOG) of eye movements, and
Unsupervised Domain Adaptation with Feature Embeddings  [PDF]
Yi Yang,Jacob Eisenstein
Computer Science , 2014,
Abstract: Representation learning is the dominant technique for unsupervised domain adaptation, but existing approaches often require the specification of "pivot features" that generalize across domains, which are selected by task-specific heuristics. We show that a novel but simple feature embedding approach provides better performance, by exploiting the feature template structure common in NLP problems.
Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation  [PDF]
Paul A. Szerlip,Gregory Morse,Justin K. Pugh,Kenneth O. Stanley
Computer Science , 2014,
Abstract: Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set. Thus DDFA features are inherently discriminative from the start even though they are trained without knowledge of the ultimate classification problem. Interestingly, DDFA also continues to add new features indefinitely (so it does not depend on a hidden layer size), is not based on minimizing error, and is inherently divergent instead of convergent, thereby providing a unique direction of research for unsupervised feature learning. In this paper the quality of its learned features is demonstrated on the MNIST dataset, where its performance confirms that indeed DDFA is a viable technique for learning useful features.
Unsupervised Feature Learning from Temporal Data  [PDF]
Ross Goroshin,Joan Bruna,Jonathan Tompson,David Eigen,Yann LeCun
Computer Science , 2015,
Abstract: Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.
Feature Extraction by Using Non-linear and Unsupervised Neural Networks  [PDF]
A. Jalil,I.M. Qureshi,A. Naveed,T.a. Cheema
Information Technology Journal , 2003,
Abstract: Feature extraction is fairly popular in pattern recognition and classification of images. In this paper we propose an unsupervised learning algorithm for neural networks that are used in feature extraction problem. These learning algorithms use genetic algorithm as a searching technique for global minimum of error performance surface and LMS algorithm for final convergence to the global minimum. These learning algorithms used Sammon`s stress as criterion for getting feature with maximum inter pattern distances and minimum intra pattern distances. A common attribute of these learning algorithms is that they are adaptive in nature, which makes them suitable in environments when the distribution of patterns in the feature space changes with respect to time.
Unsupervised Feature Selection with Adaptive Structure Learning  [PDF]
Liang Du,Yi-Dong Shen
Computer Science , 2015,
Abstract: The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods.
Unsupervised Feature Construction for Improving Data Representation and Semantics  [PDF]
Marian-Andrei Rizoiu,Julien Velcin,Stéphane Lallich
Computer Science , 2015, DOI: 10.1007/s10844-013-0235-x
Abstract: Feature-based format is the main data representation format used by machine learning algorithms. When the features do not properly describe the initial data, performance starts to degrade. Some algorithms address this problem by internally changing the representation space, but the newly-constructed features are rarely comprehensible. We seek to construct, in an unsupervised way, new features that are more appropriate for describing a given dataset and, at the same time, comprehensible for a human user. We propose two algorithms that construct the new features as conjunctions of the initial primitive features or their negations. The generated feature sets have reduced correlations between features and succeed in catching some of the hidden relations between individuals in a dataset. For example, a feature like $sky \wedge \neg building \wedge panorama$ would be true for non-urban images and is more informative than simple features expressing the presence or the absence of an object. The notion of Pareto optimality is used to evaluate feature sets and to obtain a balance between total correlation and the complexity of the resulted feature set. Statistical hypothesis testing is used in order to automatically determine the values of the parameters used for constructing a data-dependent feature set. We experimentally show that our approaches achieve the construction of informative feature sets for multiple datasets.
Adaptive Feature Ranking for Unsupervised Transfer Learning  [PDF]
Son N. Tran,Artur d'Avila Garcez
Computer Science , 2013,
Abstract: Transfer Learning is concerned with the application of knowledge gained from solving a problem to a different but related problem domain. In this paper, we propose a method and efficient algorithm for ranking and selecting representations from a Restricted Boltzmann Machine trained on a source domain to be transferred onto a target domain. Experiments carried out using the MNIST, ICDAR and TiCC image datasets show that the proposed adaptive feature ranking and transfer learning method offers statistically significant improvements on the training of RBMs. Our method is general in that the knowledge chosen by the ranking function does not depend on its relation to any specific target domain, and it works with unsupervised learning and knowledge-based transfer.
Unsupervised Feature Analysis with Class Margin Optimization  [PDF]
Sen Wang,Feiping Nie,Xiaojun Chang,Lina Yao,Xue Li,Quan Z. Sheng
Computer Science , 2015,
Abstract: Unsupervised feature selection has been always attracting research attention in the communities of machine learning and data mining for decades. In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features. Specifically, our proposed algorithm integrates the Maximum Margin Criterion with a sparsity-based model into a joint framework, where the class margin and feature correlation are taken into account at the same time. To maximize the total data separability while preserving minimized within-class scatter simultaneously, we propose to embed Kmeans into the framework generating pseudo class label information in a scenario of unsupervised feature selection. Meanwhile, a sparsity-based model, ` 2 ,p-norm, is imposed to the regularization term to effectively discover the sparse structures of the feature coefficient matrix. In this way, noisy and irrelevant features are removed by ruling out those features whose corresponding coefficients are zeros. To alleviate the local optimum problem that is caused by random initializations of K-means, a convergence guaranteed algorithm with an updating strategy for the clustering indicator matrix, is proposed to iteractively chase the optimal solution. Performance evaluation is extensively conducted over six benchmark data sets. From plenty of experimental results, it is demonstrated that our method has superior performance against all other compared approaches.
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