FEATURE SPACE UNIDIMENSIONAL PROJECTIONS FOR SCATTERPLOTS
نویسندگان
چکیده
منابع مشابه
Linear feature space projections for speaker adaptation
We extend the well-known technique of constrained Maximum Likelihood Linear Regression (MLLR) to compute a projection (instead of a full rank transformation) on the feature vectors of the adaptation data. We model the projected features with phone-dependent Gaussian distributions and also model the complement of the projected space with a single class-independent, speaker-specific Gaussian dist...
متن کاملRegression by Feature Projections
This paper describes a machine learning method, called Regression by Feature Projections (RFP), for predicting a real-valued target feature. In RFP training is based on simply storing the projections of the training instances on each feature separately. Prediction is computed through two approximation procedures. The first approximation process is to find the individual predictions of features ...
متن کاملRegression on feature projections
This paper describes a machine learning method, called Regression on Feature Projections (RFP), for predicting a real-valued target feature, given the values of multiple predictive features. In RFP training is based on simply storing the projections of the training instances on each feature separately. Prediction of the target value for a query point is obtained through two averaging procedures...
متن کاملRadial Projections for Non-Linear Feature Extraction
In this work, two new techniques for non-linear feature extraction are presented. In these techniques, new features are obtained as radial projections of the original measurements. Radial projections are a particular kind of second order transformations that show interesting properties: they capture the local structure of the data and reduce dramatically the number of parameters to estimate fro...
متن کاملComparison of input and feature space nonlinear kernel nuisance attribute projections for speaker verification
Nuisance attribute projection (NAP) was an effective method to reduce session variability in SVM-based speaker verification systems. As the expanded feature space of nonlinear kernels is usually high or infinite dimensional, it is difficult to find nuisance directions via conventional eigenvalue analysis and to do projection directly in the feature space. In this paper, two different approaches...
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: Colloquium Exactarum
سال: 2017
ISSN: 2178-8332
DOI: 10.5747/ce.2017.v09.n1.e184