Temat: similarity matrix - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Efficient face recognition based on weighted matrix distance metrics and 2DPCA algorithm
Autorzy:: Rouabhia, C.
Tebbikh, H.
Powiązania:: https://bibliotekanauki.pl/articles/229482.pdf
Data publikacji:: 2011
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: classification
similarity measure
two-dimensional PCA
weighted matrix distance
human face
Opis:: In this paper, a new similarity measure is developed for human face recognition, namely, weighted matrix distance. The key difference between this metric and the standard distances is the use of matrices and weights rather than the vectors only. The two feature matrices are obtained by two-dimensional principal component analysis (2DPCA). The weights are the inverse of the eigenvalues sorted in decreasing order of the covariance matrix of all training face matrices. Experiments are performed under illumination and facial expression variations using four face image databases: ORL, Yale, PF01 and a subset of FERET. The results demonstrate the effectiveness of the proposed weighted matrix distances in 2DPCA face recognition over the standard matrix distance metrics: Yang, Frobenius and assembled matrix distance (AMD).
Źródło:: Archives of Control Sciences; 2011, 21, 2; 207-221
1230-2384
Pojawia się w:: Archives of Control Sciences
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

Tytuł:: Predicting pairwise relations with neural similarity encoders
Autorzy:: Horn, F.
Müller, K. R.
Powiązania:: https://bibliotekanauki.pl/articles/200921.pdf
Data publikacji:: 2018
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: neural networks
kernel PCA
dimensionality reduction
matrix factorization
SVD
similarity preserving embeddings
sieci neuronowe
jądro
PCA
redukcja wymiarowości
faktoryzacja macierzy
Opis:: Matrix factorization is at the heart of many machine learning algorithms, for example, dimensionality reduction (e.g. kernel PCA) or recommender systems relying on collaborative filtering. Understanding a singular value decomposition (SVD) of a matrix as a neural network optimization problem enables us to decompose large matrices efficiently while dealing naturally with missing values in the given matrix. But most importantly, it allows us to learn the connection between data points’ feature vectors and the matrix containing information about their pairwise relations. In this paper we introduce a novel neural network architecture termed similarity encoder (SimEc), which is designed to simultaneously factorize a given target matrix while also learning the mapping to project the data points’ feature vectors into a similarity preserving embedding space. This makes it possible to, for example, easily compute out-of-sample solutions for new data points. Additionally, we demonstrate that SimEc can preserve non-metric similarities and even predict multiple pairwise relations between data points at once.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 821-830
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Informacja

Wyszukujesz frazę "similarity matrix" wg kryterium: Temat

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język