Learning Spatially Localized, Parts- Based Representation

Abstract  In this paper, we propose a novel method, called local non-negative matrix factorization (LNMF).  This gives a set of bases which not only allows a non-subtractive representation of image but also manifests localized features.

Introduction  The case of N*M image pixels, each taking a value in {0,1, …,255};there is a huge number of possible configurations:  Subspace analysis helps to reveal dimensional structures if patterns observed in high dimensional spaces.

Introduction (PCA) Principal Component Analysis (PCA)  Dimension reduction is achieved by discarding least significant components.  PCA is unable to extract basis components manifesting localized features.

Introduction (NMF) Non-negative matrix factorization (NMF) NMF 特殊的地方在於其對矩陣分解過程的非負限制。這限制會使得能 得到更好的反應原始數據的局部特徵。

Method (NMF) NMF: Constrained Non-Negative Matrix Factorization  Let a set of training images be given as an n* matrix X.  A basis image by n*m matrix B.  H is the matrix of m* coefficients of weights.  Dimension reduction is achieved when m<n. Kullback – Leibler divergence

Method (LNMF) LNMF: Given the existing constrains for all i, we wish that should be as small as possible. Imposed by =min. Different bases should be as orthogonal as possible, so as to minimize redundancy. Imposed by. Only components giving most important information should be retained. Imposed by.

Experiments Data Preparation The set of the 10 images for each person is randomly partitioned into training subset of 5 images and a test set of the other 5. The training set is then used to learn basis components, and the test set for evaluate.

Experiments Learning Basis Components

Experiments Reconstruction

Experiments Face Recognition

Experiments Face Recognition