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Case-based Reasoning (CBR) on high-dimensional and heterogeneous data is atrending yet challenging and computationally expensive task in the real world.A promising approach is to obtain low-dimensional hash codes representing casesand perform a similarity retrieval of cases in Hamming space. However, previousmethods based on data-independent hashing rely on random projections or manualconstruction, inapplicable to address specific data issues (e.g.,high-dimensionality and heterogeneity) due to their insensitivity to datacharacteristics. To address these issues, this work introduces a novel deephashing network to learn similarity-preserving compact hash codes for efficientcase retrieval and proposes a deep-hashing-enabled CBR model HeCBR.Specifically, we introduce position embedding to represent heterogeneousfeatures and utilize a multilinear interaction layer to obtain case embeddings,which effectively filtrates zero-valued features to tackle high-dimensionalityand sparsity and captures inter-feature couplings. Then, we feed the caseembeddings into fully-connected layers, and subsequently a hash layer generateshash codes with a quantization regularizer to control the quantization lossduring relaxation. To cater to incremental learning of CBR, we further proposean adaptive learning strategy to update the hash function. Extensiveexperiments on public datasets show that HeCBR greatly reduces storage andsignificantly accelerates case retrieval. HeCBR achieves desirable performancecompared with the state-of-the-art CBR methods and performs significantlybetter than hashing-based CBR methods in classification.

Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning