Dimensionality reduction using singular vectors
نویسندگان
چکیده
منابع مشابه
Language Recognition via i-vectors and Dimensionality Reduction
In this paper, a new language identification system is presented based on the total variability approach previously developed in the field of speaker identification. Various techniques are employed to extract the most salient features in the lower dimensional i-vector space and the system developed results in excellent performance on the 2009 LRE evaluation set without the need for any post-pro...
متن کاملDimensionality Reduction for Nonlinear Regression with Two Predictor Vectors
Many variables that we would like to predict depend nonlinearly on two types of attributes. For example, prices are influenced by supply and demand. Movie ratings are determined by demographic attributes and genre attributes. This paper addresses the dimensionality reduction problem in such regression problems with two predictor vectors. In particular, we assume a discriminative model where low...
متن کاملDimensionality reduction of large TDOA vectors for speaker diarization
In this work, we investigate a dimensionality reduction scheme to use Time Delay of Arrival(TDOA) features across all microphones in a traditional HMM/GMM system. The subspace dimension is selected based on dimension of the TDOA vectors in an ideal recording, i.e., without environmental distortion or interference. Experiments in a dataset used in NIST Meeting Diarization evaluation reveal that ...
متن کاملImage Reduction Using Assorted Dimensionality Reduction Techniques
Dimensionality reduction is the mapping of data from a high dimensional space to a lower dimension space such that the result obtained by analyzing the reduced dataset is a good approximation to the result obtained by analyzing the original data set. There are several dimensionality reduction approaches which include Random Projections, Principal Component Analysis, the Variance approach, LSA-T...
متن کاملDimensionality Reduction using Relative Attributes
Visual attributes are high-level semantic description of visual data that are close to the language of human. They have been intensively used in various applications such as image classification [1,2], active learning [3,4], and interactive search [5]. However, the usage of attributes in dimensionality reduction has not been considered yet. In this work, we propose to utilize relative attribute...
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: Scientific Reports
سال: 2021
ISSN: 2045-2322
DOI: 10.1038/s41598-021-83150-y