the principle of dimensionality reduction with pca is the representation of the dataset ‘x’in terms of eigenvectors ei ∈ rn  of its covariance matrix. the eigenvectors oriented in the direction with the maximum variance of x in rn carry the most      relevant information of x. these eigenvectors are called principal components [8]. assume that n images in a set are originally represented in mat...

Identi cation of independent physical/dynamical modes and corresponding principal component time series is an important aspect of climate studies for they serve as a tool for detecting and predicting climate changes. While there are a number of di erent eigen techniques their performance for identifying independent modes varies. Considered here are comparison tests of eight eigen techniques in ...

In this paper, we introduce a new method for elliptical object identification. The proposed method adopts a hybrid scheme which consists of Eigen values of covariance matrices, Circular Hough transform and Bresenham’s raster scan algorithms. In this approach we use the fact that the large Eigen values and small Eigen values of covariance matrices are associated with the major and minor axial le...

In this paper, we propose an anchor modeling scheme where instead of conventional “anchor” speakers, we use eigenvectors that span the Eigen-voice space. The computational advantage of conventional Anchor-modeling based speaker identification system comes from representing all speakers in a space spanned by a small number of anchor speakers instead of having separate speaker models. The convent...