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| Baback Moghaddam, Alex Pentland, "Probabilistic Visual Learning for Object Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696-710, July, 1997. | |||
| BibTex | x | ||
| @article{ 10.1109/34.598227, author = {Baback Moghaddam and Alex Pentland}, title = {Probabilistic Visual Learning for Object Representation}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {19}, number = {7}, issn = {0162-8828}, year = {1997}, pages = {696-710}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.598227}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Probabilistic Visual Learning for Object Representation IS - 7 SN - 0162-8828 SP696 EP710 EPD - 696-710 A1 - Baback Moghaddam, A1 - Alex Pentland, PY - 1997 KW - Face recognition KW - gesture recognition KW - target detection KW - subspace methods KW - maximum-likelihood KW - density estimation KW - principal component analysis KW - Eigenfaces. VL - 19 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—We present an unsupervised technique for visual learning, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a
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