  To analyze marine animals behavior, seasonal distribution and abundance,
digital imagery can be acquired by visual or Lidar camera. Depending on the
quantity and properties of acquired imagery, the animals are characterized as
either features (shape, color, texture, etc.), or dissimilarity matrices
derived from different shape analysis methods (shape context, internal distance
shape context, etc.). For both cases, multi-view learning is critical in
integrating more than one set of feature or dissimilarity matrix for higher
classification accuracy. This paper adopts correntropy loss as cost function in
multi-view learning, which has favorable statistical properties for rejecting
noise. For the case of features, the correntropy loss-based multi-view learning
and its entrywise variation are developed based on the multi-view intact space
learning algorithm. For the case of dissimilarity matrices, the robust
Euclidean embedding algorithm is extended to its multi-view form with the
correntropy loss function. Results from simulated data and real-world marine
animal imagery show that the proposed algorithms can effectively enhance
classification rate, as well as suppress noise under different noise
conditions.
