With　the　booming　development　of　the　cyber-physical　system,　human　society　is　much　more　dependent　on　information　technology.　Unfortunately,　like　software,　hardware　is　not　trusted　at　all,　due　to　so　many　third　parties　involved　in　the　separated　integrated　circuit＇s　(IC)　design　and　manufacturing　stages　for　the　high　profit.　The　malicious　circuits　(named　Hardware　Trojans)　can　be　implanted　during　any　stage　of　the　ICs＇　design　and　manufacturing　process.　However,　the　existing　pre-silicon　approaches　based　on　machine　learning　theory　have　good　performance,　they　all　belong　to　supervised　learning　methods,　which　have　a　key　prerequisite　that　is　numerous　already　known　information.　Meanwhile,　hardware　Trojans　are　even　more　unimaginable　because　today＇s　ICs　are　becoming　more　complicated.　The　known　information　is　even　harder　to　gain.　Furthermore,　the　training　process　for　supervised　learning　methods　tends　to　be　time-consuming　and　generally　requires　a　huge　amount　of　balanced　training　data.　Therefore,　this　paper　proposes　an　unsupervised　hardware　Trojans　detection　approach　by　combined　the　principal　component　analysis　(PCA)　and　local　outlier　factor　(LOF)　algorithm,　called　PL-HTD.　We　firstly　visualize　the　distribution　features　of　normal　nets　and　Trojan　nets,　and　then　reveal　the　differences　between　the　two　types　of　nets　to　reduce　the　dimension　of　the　feature　set.　According　to　the　outliers　of　each　net,　the　abnormal　nets　are　selected　and　verified　by　professionals　later　to　confirm　whether　it　is　a　true　Trojan　relative　to　the　host　circuit　to　realize　the　detection.　The　experiments　show　that　the　proposed　method　can　detect　hardware　Trojans　effectively　and　reduce　the　cost　of　manual　secondary　detection.　For　the　Trust-HUB　benchmarks,　the　PL-HTD　achieves　up　to　73.08%　TPR　and　97.52%　average　TNR,　moreover,　it　achieves　average　96.00%　accuracy,　which　shows　the　feasibility　and　efficiency　of　hardware　Trojans　detecting　by　employing　a　method　without　the　guidance　of　class　label　information.