PQk-means: Billion-scale Clustering for Product-quantized Codes
This addresses the need for scalable clustering in memory-restricted environments, such as big data analysis, with incremental improvements over standard k-means.
The paper tackles the problem of clustering large-scale data efficiently by proposing PQk-means, which compresses vectors into product-quantized codes to enable fast and memory-efficient clustering, achieving clustering of one billion 128D SIFT features in 14 hours with 32 GB memory.
Data clustering is a fundamental operation in data analysis. For handling large-scale data, the standard k-means clustering method is not only slow, but also memory-inefficient. We propose an efficient clustering method for billion-scale feature vectors, called PQk-means. By first compressing input vectors into short product-quantized (PQ) codes, PQk-means achieves fast and memory-efficient clustering, even for high-dimensional vectors. Similar to k-means, PQk-means repeats the assignment and update steps, both of which can be performed in the PQ-code domain. Experimental results show that even short-length (32 bit) PQ-codes can produce competitive results compared with k-means. This result is of practical importance for clustering in memory-restricted environments. Using the proposed PQk-means scheme, the clustering of one billion 128D SIFT features with K = 10^5 is achieved within 14 hours, using just 32 GB of memory consumption on a single computer.