query: Eigenvectors of 3D image data (eigenfaces)
positive_passages.docid: 156868
positive_passages.text: Why is finding $M$ eigenvectors on smaller matrix valid?
negative_passages.docid: 70223
negative_passages.text: Inducing orientations on boundary manifolds
negative_passages.docid: 119662
negative_passages.text: understanding projection matrices
negative_passages.docid: 158716
negative_passages.text: Cholesky versus eigendecomposition for drawing samples from a multivariate normal distribution
negative_passages.docid: 114923
negative_passages.text: Speeding up generation of block diagonal matrix
negative_passages.docid: 151088
negative_passages.text: Finding Matrix A from Eigenvalues and Eigenvectors (Diagonalization)
negative_passages.docid: 77090
negative_passages.text: Feature map for the Gaussian kernel
negative_passages.docid: 52260
negative_passages.text: Diagonalization and eigenvalues
negative_passages.docid: 86150
negative_passages.text: Geometric interpretation of matrices
negative_passages.docid: 14465
negative_passages.text: "Normalizing" variables for SVD / PCA
negative_passages.docid: 126544
negative_passages.text: Constructing N-dimensional vectors out of point distances
negative_passages.docid: 49158
negative_passages.text: Multiplicity of eigenvalues
negative_passages.docid: 72375
negative_passages.text: Efficiently Constructing Rank One Approximations for a Matrix using SVD
negative_passages.docid: 127285
negative_passages.text: Inverse images of images, images of inverse images of a set of vectors
negative_passages.docid: 130574
negative_passages.text: Distance metric for (infinite) lines in 3D
negative_passages.docid: 90595
negative_passages.text: Haar Cascaded Classifier Data in OpenCV:
negative_passages.docid: 98369
negative_passages.text: Reducing number of point features considering minimal distance and spatial distribution