Learning Pairwise Similarity
ثبت نشده
چکیده
Overview Different vectorial and / or (dis)similarity representations can be produced for a given data. These distinct representations or data generating models have typically been used individually, in single classifiers or single clustering algorithms, or simultaneously, as in classifier combination techniques or cluster ensemble methods, depending, respectively, on whether working under a supervised or unsupervised learning approach.
منابع مشابه
Discriminative Similarity for Clustering and Semi-Supervised Learning
Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning...
متن کاملOn a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification
Pairwise clustering methods partition the data space into clusters by the pairwise similarity between data points. The success of pairwise clustering largely depends on the pairwise similarity function defined over the data points, where kernel similarity is broadly used. In this paper, we present a novel pairwise clustering framework by bridging the gap between clustering and multi-class class...
متن کاملTransfer Regression via Pairwise Similarity Regularization
Transfer learning methods address the situation where little labeled training data from the “target” problem exists, but much training data from a related “source” domain is available. However, the overwhelming majority of transfer learning methods are designed for simple settings where the source and target predictive functions are almost identical, limiting the applicability of transfer learn...
متن کاملInstance Similarity Deep Hashing for Multi-Label Image Retrieval
Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional featurelearning-based methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-a...
متن کاملA New Framework for Dissimilarity and Similarity Learning
In this work we propose a novel framework for learning a (dis)similarity function. We cast the learning problem as a binary classification task or a regression task in which the new learning instances are the pairwise absolute differences of the original instances. Under the classification approach the class label we assign to a specific pairwise difference indicates whether the two original in...
متن کاملLearning Texture Similarity with Perceptual Pairwise Distance
In this paper, we demonstrate how texture classification and retrieval could benefit from learning perceptual pairwise distance of different texture classes. Textures as represented by certain image features may not be correctly compared in a way that is consistent with human perception. Learning similarity helps to alleviate this perceptual inconsistency. For textures, psychological experiment...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008