query: Truncated SVD: how do I go from [Uk, Sk, Vk'] to low-dimension matrix?
positive_passages.docid: 7603
positive_passages.text: Relationship between SVD and PCA. How to use SVD to perform PCA?
negative_passages.docid: 109090
negative_passages.text: Inverting non positive definite covariance matrix
negative_passages.docid: 602
negative_passages.text: How get eigenvectors without phase jump?
negative_passages.docid: 68419
negative_passages.text: Can I return lists with different dimensions from a compiled function?
negative_passages.docid: 125878
negative_passages.text: Confusion when going from index notation to matrix notation
negative_passages.docid: 148862
negative_passages.text: Is reducing a matrix to row echelon form useful at all?
negative_passages.docid: 71313
negative_passages.text: Why is nls() giving me "singular gradient matrix at initial parameter estimates" errors?
negative_passages.docid: 142623
negative_passages.text: scaling for SVM destroys my results
negative_passages.docid: 22943
negative_passages.text: How to convert from high poly to low poly
negative_passages.docid: 130286
negative_passages.text: Problem with Singular Value Decomposition
negative_passages.docid: 145885
negative_passages.text: Linear transformation preserving orthogonality
negative_passages.docid: 152242
negative_passages.text: How can I map data to lower dimension?
negative_passages.docid: 126441
negative_passages.text: Zero-truncated Poisson model
negative_passages.docid: 81783
negative_passages.text: Labeling all sides of the matrix with kbordermatrix
negative_passages.docid: 20406
negative_passages.text: From ND to 1D arrays
negative_passages.docid: 91938
negative_passages.text: Countable Matrix Representation
negative_passages.docid: 46319
negative_passages.text: How to perform orthogonal regression (total least squares) via PCA?