Binary Matrix Factorization with Applications Chinese Academy of Sciences Chinese Academy of SciencesZhang, ZhongyuanDing, Chris
Most of these applications contain a lot of features so that features' extraction is an important step in the process because of the huge size of raw data. Binary Matrix Factorization is a common method for extracting non-traditional features from the data to be processed. It is a powerful ...
The author of [45] proposed a method to extends non-negative matrix factorization to obtain occlusion estimation. The method does not require the position of occlusion parts. In [46], the author proposed a model that adds MaskNet at the traditional CNN model’s middle layer. The goal is ...
"//tensorflow/contrib/factorization:factorization_py", "//tensorflow/contrib/feature_column:feature_column_py", "//tensorflow/contrib/framework:framework_py", "//tensorflow/contrib/gan", "//tensorflow/contrib/graph_editor:graph_editor_py", "//tensorflow/contrib/grid_rnn:grid_rnn_py", "//tensor...
Compute the Permanent of a Matrix with Ryser's algorithm Thepermanentof a Matrix is similar to the determinant, but in the definition the signatures of the permutations are ignored. Ryser's algorithm is an efficient method for computing the permanent. ...
Binary matrix completion. The left matrix is the latent preference matrix. The right matrix is the observed matrix, in which the observed entries are labelled with ‘+ 1’ if an MDA is found, with ‘-1’ if a non-MDA is found, and ‘?’ if the entry is not observed ...
the one with generator matrix (1,i), where i denotes a square root of −1 in Fq. This enables one to characterize the self-dual quasicyclic codes over Fq of length 2m and of index 2, where m is relatively prime to q, once the irreducible factors of Ym−1 are known. Proposition...
Subfigures d,e,f from Fig.4were generated as follows. We generated 100 matricesWto beW = X + X⊤, whereXis a random matrix with i. i. d. elements from the uniform distribution over [0, 1]. For each TSP instance we repeated the procedure as inW ≡ 0 case, howev...
Ever since the pioneering work of compressed sensing (CS) [1], it has garnered significant attention and found widespread applications. Mathematically, CS involves obtaining infinite-precision real-valued measurement y=Φx of a sparse signal x∈Rn via a measurement matrix Φ∈Rm×n satisfying m...
[7] to analyze the convergence and smoothness of the curves and surfaces generated by linear uniform refinement schemes. This method requires the subdivision mask and factorization of the Laurent polynomial of the given refinement scheme. The Laurent polynomial method with several modifications was used...