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The affinity matrix is of importance as it naturally encodes the cluster/subspace membership of each projected shape sample. Although exhibiting superior performance to Dai et al.'s approach for 3D structures that do not adhere to the low-rank assumption, the approach requires prior knowl- edge ...
Given a sample, the class membership probabilities of the temporary ensemble ℓˆ(Ct′) that is the probability of selecting a label by the temporary ensemble on Dt, is then calculated. La- bels are then sampled from this distribution, such that the probability of selecting a label is ...
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The concept weights U are then encoded as one-hot cluster membership vectors which we found to work better than alternatives such as similarity of a sample to each cluster center. Concept weight branch. Creating a predefined set of concepts to learn, either using dictionaries or K-means ...
Specif- ically, F = [q1, . . . , qk] ∈ Rn×k is a binary matrix in- dicating the membership of each data point to a specific group. F1 = 1 means that each data point appears in one and only one subspace. The constraint rank(F) = k is to ensure that the number ...
Other works create the label distribution by computing the membership degrees to the la- bels [13, 24, 46, 34, 22]. Recently, Chen et al. [5] leverages the topology in facial landmarks and action units spaces to acquire more information for label distrib...
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