Feature (machine learning) | EPFL Graph Search

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression. Feature types In feature engineering, two types of features are commonly used: numerical and categorical. Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning algorithms directly. Categorical features are discrete values that can be grouped into categories. Examples of categorical features include gend

Improved Phone Posterior Estimation Through k-NN and MLP-Based Similarity

Benjamin Picart

In this work, we investigate the possible use of k-nearest neighbour (kNN) classifiers to perform frame-based acoustic phonetic classification, hence replacing Gaussian Mixture Models (GMM) or MultiLayer Perceptrons (MLP) used in standard Hidden Markov Models (HMMs). The driving motivation behind this idea is the fact that kNN is known to be an "optimal" classifier if a very large amount of training data is available (replacing the training of functional parameters by plain memorization of the training examples) and the correct distance metric is found. Nowadays, amount of training data is no longer an issue. In the current work, we thus specifically focused on the "correct" distance metric, mainly using an MLP to compute the probability that two input feature vectors are part of the same phonetic class or not. This MLP output can thus be used as a distance metric for kNN. While providing a "universal" distance metric, this work also enabled us to consider the speech recognition problem under a different angle, simply formulated in terms of hypothesis tests: "Given two feature vectors, what is the probability that these belong to the same (phonetic) class or not?". Actually, one of the main goals of the present thesis finally boils down to one interesting question: “Is it easier to classify feature vectors into C phonetic classes or to tell whether or not two feature vectors belong to the same class?”. This work was done with standard acoustic features as inputs (PLP) and with posterior features (resulting of another pre-training MLP). Both feature sets indeed exhibit different properties and metric spaces. For example, while the use of posteriors as input is motivated by the fact that they are speaker and environment independent (so they capture much of the phonetic information contained in the signal), they are also no longer Gaussian distributed. When showing mathematically that using the MLP as a similarity measure makes sense, we discovered that this measure was equivalent to a very simple metric that can be analytically computed without needing the use of an MLP. This new type of measure is in fact the scalar product between two posterior feature vectors. Experiments have been conducted on hypothesis tests and on kNN classification. Results of the hypothesis tests show that posterior feature vectors achieve better performance than acoustic feature vectors. Moreover, the use of the scalar product leads to better performance than the use of all other metrics (including the MLP-based distance metric), whatever the input features.

Idiap2009

New support vector algorithms for multicategorical data applied to real-time object recognition

Silvio Borer

Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithms to learn from visual data is a difficult task. More than fifty years ago, Rosenblatt proposed the perceptron algorithm. The perceptron learns from data examples a linear separation, which categorises the data in two classes. The algorithm served as a simple model of neuronal learning. Two further important ideas were added to the perceptron. First, to look for a maximal margin of separation. Second, to separate the data in a possibly high dimensional feature space, related nonlinearly to the initial space of the data, and allowing nonlinear separations. Important is that learning in the feature space can be performed implicitly and hence efficiently with the use of a kernel, a measure of similarity between two data points. The combination of these ideas led to the support vector machine, an efficient algorithm with high performance. In this thesis, we design an algorithm to learn the categorisation of data into multiple classes. This algorithm is applied to a real-time vision task, the recognition of human faces. Our algorithm can be seen as a generalisation of the support vector machine to multiple classes. It is shown how the algorithm can be efficiently implemented. To avoid a large number of small but time consuming updates of the variables limited accuracy computations are used. We prove a bound on the accuracy needed to find a solution. The proof motivates the use of a heuristic, which further increases efficiency. We derive a second implementation using a stochastic gradient descent method. This implementation is appealing as it has a direct interpretation and can be used in an online setting. Conceptually our approach differs from standard support vector approaches because examples can be rejected and are not necessarily attributed to one of the categories. This is natural in the context of a vision task. At any time, the sensory input can be something unseen before and hence cannot be recognised. Our visual data are images acquired with the recently developed adaptive vision sensor from CSEM. The vision sensor has two important features. First, like the human retina, it is locally adaptive to light intensity. Hence, the sensor has a high dynamic range. Second, the image gradient is computed on the sensor chip and is thus available directly from the sensor in real time. The sensor output is time encoded. The information about a strong local contrast is transmitted rst and the weakest contrast information at the end. To recognise faces, possibly moving in front of the camera, the sensor images have to be processed in a robust way. Representing images to exhibit local invariances is a common yet unsolved problem in computer vision. We develop the following representation of the sensor output. The image gradient information is decomposed into local histograms over contrast intensity. The histograms are local in position and direction of the gradient. Hence, the representation has local invariance properties to translation, rotation, and scaling. The histograms can be efficiently computed because the sensor output is already ordered with respect to the local contrast. Our support vector approach for multicategorical data uses the local histogram features to learn the recognition of faces. As recognition is time consuming, a face detection stage is used beforehand. We learn the detection features in an unsupervised manner using a specially designed optimisation procedure. The combined system to detect and recognise faces of a small group of individuals is efficient, robust, and reliable.

EPFL2003

Investigation of kNN Classifier on Posterior Features Towards Application in Automatic Speech Recognition

Afsaneh Asaei, Hervé Bourlard, Benjamin Picart

Class posterior distributions can be used to classify or as intermediate features, which can be further exploited in different classifiers (e.g., Gaussian Mixture Models, GMM) towards improving speech recognition performance. In this paper we examine the possibility to use kNN classifier to perform local phonetic classification of class posterior distribution extracted from acoustic vectors. In that framework, we also propose and evaluate a new kNN metric based on the relative angle between feature vectors to define the nearest neighbors. This idea is inspired by the orthogonality characteristic of the posterior features. To fully exploit this attribute, kNN is used in two main steps: (1) the distance is computed as the cosine function of the relative angle between the test vector and the training vector and (2) the nearest neighbors are defined as the samples within a specific relative angle to the test data and the test samples which do not have enough labels in such a hyper-cone are considered as uncertainties and left undecided. This approach is evaluated on TIMIT database and compared to other metrics already used in literature for measuring the similarity between posterior probabilities. Based on our experiments, the proposed approach yield 78.48% frame level accuracy while specifying 15.17% uncertainties in the feature space.

Idiap2010