منابع مشابه
Density-Sensitive Semisupervised Inference
Semisupervised methods are techniques for using labeled data make predictions. These methods invoke some assumptions that link the marginal distribution PX of X to the regression function f (x). For example, it is common to assume that f is very smooth over high density regions of PX. Many of the methods are ad-hoc and have been shown to work in specific examples but are lacking a theoretical f...
متن کاملSemisupervised learning using feature selection based on maximum density subgraphs
We present a new graph based semi-supervised learning algorithm, using multiway cut on a neighborhood graph to achieve an optimum classification. We also present a graph based feature selection algorithm utilizing the global structure of the graph derived from both labeled and unlabeled examples. With respect to the experiments we conducted, both of our approaches are proved to have a promising...
متن کاملSemisupervised Multimodal Dimensionality Reduction
The problem of learning from both labeled and unlabeled data is considered. In this paper, we present a novel semisupervised multimodal dimensionality reduction (SSMDR) algorithm for feature reduction and extraction. SSMDR can preserve the local and multimodal structures of labeled and unlabeled samples. As a result, data pairs in the close vicinity of the original space are projected in the ne...
متن کاملTowards context sensitive information inference
Humans can make hasty, but generally robust judgements about what a text fragment is, or is not, about. Such judgements are termed information inference. By drawing on theories from non-classical logic and applied cognition, an information inference mechanism is proposed which makes inferences via computations of information flow through a high dimensional conceptual space. Within a conceptual ...
متن کاملDensity-Sensitive Evolutionary Clustering
In this study, we propose a novel evolutionary algorithm-based clustering method, named density-sensitive evolutionary clustering (DSEC). In DSEC, each individual is a sequence of real integer numbers representing the cluster representatives, and each data item is assigned to a cluster representative according to a novel density-sensitive dissimilarity measure which can measure the geodesic dis...
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: The Annals of Statistics
سال: 2013
ISSN: 0090-5364
DOI: 10.1214/13-aos1092