Principal subspace estimation for low-rank Toeplitz covariance matrices with binary sensing.
Haoyu FuYuejie ChiPublished in: ACSSC (2016)
Keyphrases
- covariance matrices
- low rank
- covariance matrix
- regularized regression
- maximum likelihood
- low rank representation
- missing data
- linear combination
- high dimensional data
- singular value decomposition
- matrix factorization
- gaussian distribution
- rank minimization
- convex optimization
- vector space
- kernel matrix
- gaussian mixture model
- distance measure
- semi supervised
- high order
- least squares
- feature vectors
- gaussian mixture
- low dimensional
- linear classifiers
- density estimation
- high dimensional
- dimensionality reduction
- euclidean space
- principal component analysis
- data points