Deep Subclass Linear Discriminant Analysis For Multimodal Feature Space Learning

Proceedings - International Conference on Image Processing, vol. abs 1511 4707, pp. 1701–1705

Abstract

In this work, we target a known problem in representation learning that is: beyond coarse classification, how can we better model fine-grained categorization? To address this problem, we introduce Deep Subclass Linear Discriminant Analysis (DeepSDA), which utilizes intra-class variation and inter-class similarity during training. We could achieve multimodal classification by maximizing the ratio of between-subclass scatter matrix and within-subclass scatter matrix. We maximize the eigenvalues along the discriminative eignevector directions. Hence the deep neural network is able to learn more discriminative representation space and thus has higher class separation in the linearly separable latent space. We show that DeepSDA leads to significant improvements on diverse fine-grained categorization and attribute learning benchmarks.

Authors 4

1. Abin Jose Aachen Institut fur Nachrichtentechnik

   RWTH Aachen University

   Affiliation as printed

   Institut fur Nachrichtentechnik, RWTH Aachen University, Aachen, Germany

2. Yan Shen

   Michigan State University

   Affiliation as printed

   Michigan State University, East Lansing, USA

3. Mi Zhang

   Michigan State University

   Affiliation as printed

   Michigan State University, East Lansing, USA

4. Jens-Rainer Ohm Aachen Institut fur Nachrichtentechnik

   RWTH Aachen University

   Affiliation as printed

   Institut fur Nachrichtentechnik, RWTH Aachen University, Aachen, Germany