Dissimilarity learning for nominal data
نویسندگان
چکیده
منابع مشابه
Dissimilarity learning for nominal data
Defining a good distance (dissimilarity) measure between patterns is of crucial importance in many classification and clustering algorithms. While a lot of work has been performed on continuous attributes, nominal attributes are more difficult to handle. A popular approach is to use the value difference metric (VDM) to define a real-valued distance measure on nominal values. However, VDM treats...
متن کاملDissimilarity-based learning for complex data
Rapid advances of information technology have entailed an ever increasing amount of digital data, which raises the demand for powerful data mining and machine learning tools. Due to modern methods for gathering, preprocessing, and storing information, the collected data become more and more complex: a simple vectorial representation, and comparison in terms of the Euclidean distance is often no...
متن کاملSupervised Generative Models for Learning Dissimilarity Data
Exemplar based techniques such as affinity propagation [1] represent data in terms of typical exemplars. This has two benefits: (i) the resulting models are directly interpretable by humans since representative exemplars can be inspected in the same way as data points, (ii) the model can be applied to any dissimilarity measure including non-Euclidean or non-metric settings. Most exemplar based ...
متن کاملSemisupervised learning from dissimilarity data
The following two-stage approach to learning from dissimilarity data is described: (1) embed both labeled and unlabeled objects in a Euclidean space; then (2) train a classifier on the labeled objects. The use of linear discriminant analysis for (2), which naturally invites the use of classical multidimensional scaling for (1), is emphasized. The choice of the dimension of the Euclidean space i...
متن کاملDissimilarity Data in Statistical Model Building and Machine Learning
We explore three papers concerned with two methods for incorporating discrete, noisy, incomplete dissimilarity data into statistical/machine learning models for supervised, semisupervised or unsupervised machine learning. The two methods are RKE (Regularized Kernel Estimation), and RMU (Regularized Manifold Unfolding). Briefly put, the methods use dissimilarity information between objects in a ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2004
ISSN: 0031-3203
DOI: 10.1016/s0031-3203(04)00041-x