Learning with Partially Labeled and Interdependent Data

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The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus do not meet the demands of many web-related tasks.

Later chapters deal with the development of learning methods for ranking entities in a large collection with respect to precise information needed. In some cases, learning a ranking function can be reduced to learning a classification function over the pairs of examples. The book proves that this task can be efficiently tackled in a new framework: learning with interdependent data. Researchers and professionals in machine learning will find these new perspectives and solutions valuable.

Learning with Partially Labeled and Interdependent Data is also useful for advanced-level students of computer science, particularly those focused on statistics and learning. Keywords : Semi-supervised learning Learning with interdependent data learning to rank. Document type : Books. In the case where there are more than two classes, the analysis used in the derivation of the Fisher discriminant can be extended to find a subspace which appears to contain all of the class variability.

Then the scatter between class variability may be defined by the sample covariance of the class means. These eigenvectors are primarily used in feature reduction, as in PCA. The eigenvectors corresponding to the smaller eigenvalues will tend to be very sensitive to the exact choice of training data, and it is often necessary to use regularisation as described in the next section. If classification is required, instead of dimension reduction , there are a number of alternative techniques available.

For instance, the classes may be partitioned, and a standard Fisher discriminant or LDA used to classify each partition. A common example of this is "one against the rest" where the points from one class are put in one group, and everything else in the other, and then LDA applied. This will result in C classifiers, whose results are combined.

The typical implementation of the LDA technique requires that all the samples are available in advance. However, there are situations where the entire data set is not available and the input data are observed as a stream. In this case, it is desirable for the LDA feature extraction to have the ability to update the computed LDA features by observing the new samples without running the algorithm on the whole data set.

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For example, in many real-time applications such as mobile robotics or on-line face recognition, it is important to update the extracted LDA features as soon as new observations are available. An LDA feature extraction technique that can update the LDA features by simply observing new samples is an incremental LDA algorithm , and this idea has been extensively studied over the last two decades. In practice, the class means and covariances are not known. They can, however, be estimated from the training set. Either the maximum likelihood estimate or the maximum a posteriori estimate may be used in place of the exact value in the above equations.

Although the estimates of the covariance may be considered optimal in some sense, this does not mean that the resulting discriminant obtained by substituting these values is optimal in any sense, even if the assumption of normally distributed classes is correct. Another complication in applying LDA and Fisher's discriminant to real data occurs when the number of measurements of each sample i. There are a number of ways to deal with this.

One is to use a pseudo inverse instead of the usual matrix inverse in the above formulae. This leads to the framework of regularized discriminant analysis [23] or shrinkage discriminant analysis. Also, in many practical cases linear discriminants are not suitable. LDA and Fisher's discriminant can be extended for use in non-linear classification via the kernel trick.

Here, the original observations are effectively mapped into a higher dimensional non-linear space. Linear classification in this non-linear space is then equivalent to non-linear classification in the original space.

Linear discriminant analysis

The most commonly used example of this is the kernel Fisher discriminant. LDA can be generalized to multiple discriminant analysis , where c becomes a categorical variable with N possible states, instead of only two.

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These projections can be found by solving a generalized eigenvalue problem , where the numerator is the covariance matrix formed by treating the means as the samples, and the denominator is the shared covariance matrix. In addition to the examples given below, LDA is applied in positioning and product management. In bankruptcy prediction based on accounting ratios and other financial variables, linear discriminant analysis was the first statistical method applied to systematically explain which firms entered bankruptcy vs.

Despite limitations including known nonconformance of accounting ratios to the normal distribution assumptions of LDA, Edward Altman 's model is still a leading model in practical applications. In computerised face recognition , each face is represented by a large number of pixel values.

Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template. The linear combinations obtained using Fisher's linear discriminant are called Fisher faces , while those obtained using the related principal component analysis are called eigenfaces. Logistic regression or other methods are now more commonly used.

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The use of discriminant analysis in marketing can be described by the following steps:. The main application of discriminant analysis in medicine is the assessment of severity state of a patient and prognosis of disease outcome. For example, during retrospective analysis, patients are divided into groups according to severity of disease — mild, moderate and severe form. Then results of clinical and laboratory analyses are studied in order to reveal variables which are statistically different in studied groups.

Using these variables, discriminant functions are built which help to objectively classify disease in a future patient into mild, moderate or severe form. In biology, similar principles are used in order to classify and define groups of different biological objects, for example, to define phage types of Salmonella enteritidis based on Fourier transform infrared spectra, [25] to detect animal source of Escherichia coli studying its virulence factors [26] etc. This method can be used to separate the alteration zones.

For example, when different data from various zones are available, discriminant analysis can find the pattern within the data and classify it effectively. Discriminant function analysis is very similar to logistic regression , and both can be used to answer the same research questions. Geometric anomalities in high dimension lead to the well-known curse of dimensionality. Nevertheless, proper utilization of concentration of measure phenomena can make computation easier.

Data separability by classical linear discriminants simplifies the problem of error correction for artificial intelligence systems in high dimension.. From Wikipedia, the free encyclopedia.

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Not to be confused with latent Dirichlet allocation. Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random field Hidden Markov.

Anomaly detection. Artificial neural networks. Reinforcement learning. Machine-learning venues. Glossary of artificial intelligence. Related articles.

List of datasets for machine-learning research Outline of machine learning. Annals of Eugenics. Discriminant Analysis and Statistical Pattern Recognition.

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Wiley Interscience. Salkind Ed. Thousand Oaks CA : Sage. Computer Methods and Programs in Biomedicine. Discriminant function analysis: Concept and application. Salkind, N. New Jersey: Prentice Hall. Modern Applied Statistics with S 4th ed. Springer Verlag. Discriminant analysis. Applied Multivariate Statistical Analysis.

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Springer Berlin Heidelberg. Discriminant function analysis. June Geocarto International. Archived from the original on Retrieved CS1 maint: archived copy as title link. Pattern Recognition. Pattern Recogn.